Uploaded byForward2025
PDF, PPTX2,205 views

Radar 2009 a 16 parameter estimation and tracking part2

This document summarizes a lecture on parameter estimation and tracking. It discusses tracking processes like track association, initiation, maintenance through prediction and updating, and termination. Filtering techniques like the Kalman filter are presented as ways to estimate target position and velocity while accounting for noise and maneuvers. Examples of civilian and military target maneuvers are provided to illustrate the challenges of tracking.

Embed presentation

Download as PDF, PPTX
IEEE New Hampshire Section Radar Systems Course 1 Tracking 1/1/2010 IEEE AES Society Radar Systems Engineering Lecture 16 Parameter Estimation and Tracking Part 2 Dr. Robert M. O’Donnell IEEE New Hampshire Section Guest Lecturer
Radar Systems Course 2 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Pulse Compression Receiver Clutter Rejection (Doppler Filtering) A / D Converter Block Diagram of Radar System Antenna Propagation Medium Target Radar Cross Section Transmitter General Purpose Computer Tracking Data Recording Parameter Estimation Waveform Generation Detection Power Amplifier T / R Switch Signal Processor Computer Thresholding User Displays and Radar Control Photo Image Courtesy of US Air Force
Radar Systems Course 3 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
Radar Systems Course 4 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Multiple Target Tracking Radars Courtesy of USAF Courtesy of FAACourtesy of FAA Courtesy of Raytheon, Used with Permission.Courtesy of NATOCourtesy of NATO ASR-9Patriot AWACSRussian “FLAP LID” S-300 Courtesy of Martin Rosenkrantz, Used with PermissionCourtesy of Martin Rosenkrantz, Used with Permission Courtesy of US Air ForceCourtesy of US Air Force
Radar Systems Course 5 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Radar Tracking Example • Tracker receives new observations every scan – Target observations – False alarms Tracker Input Tracker Output RangeRange Cross-Range • New tracks are initiated • Existing tracks are updated • Obsolete tracks are deleted New Track Existing Track Observations True Target Position Courtesy of MIT Lincoln Laboratory Used with Permission
Radar Systems Course 6 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
Radar Systems Course 7 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track File (Track States) Block Diagram of Tracking Process Detection Threshold Crossings Adaptive Area CFAR Track Termination Track Association ? Detection Consolidation Track Update Track Prediction Track Initiation Radar Scheduling Control Track Update Rate Monitoring For Phased Array Radars New Raw Target Reports Filtered Raw Target Reports Non-associated Reports Existing TracksYes Yes No New Tracks Track Formation To Radar Control Tracks
Radar Systems Course 8 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Simplified Tracking Tasks • Track Association – Associate new observations with existing tracks • Track Initiation – Initiate new tracks on non- associated observations (two or three scans) • Track Maintenance – Update a smoothed (filtered) estimate of the target’s present state – Predict new estimate of target’s state on the next scan • Track Termination – Terminate tracks that are missing new observations for a number of scans Existing Tracks New Tracks New Observations Non-associated Observations Detection Reports: range, range rate, azimuth, elevation, etc. Association PredictionInitiation Update Termination Courtesy of MIT Lincoln Laboratory Used with Permission
Radar Systems Course 9 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Goal of the Tracking Process • Input – Raw detections from the radar Threshold crossings for each range, azimuth, • Output – State vector for each viable target and its predicted state vector at a later time State vector = – Amplitude information – Track “quality” information i.e. track life, etc. • Few, if any false tracks !! t,z,y,x,z,y,x &&&
Radar Systems Course 10 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Detection Consolidation • Often, a target may produce many adjacent threshold crossings • Adjacency in range, angle and Doppler velocity are usually used as the criteria for grouping clusters of detections • The sets of threshold crossings (and their amplitudes) are then used to interpolation to determine a more accurate value of each measured observables – Weighted average – Fit data to the expected angle beam shape (angle), pulse response (range), or Doppler filter shape (Doppler velocity) To radar 1/16 nmi Target Detections From 4 CPI’s 1Beamwidth -60 kts 0 60 kts dB 0 -80 -60 -40 -20 Noise Level -60 kts 0 60 kts dB 0 -20 -40 -60 -80 Noise Level Commercial Airliner Two Engine Propeller Aircraft Aircraft Doppler Spectra
Radar Systems Course 11 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Adaptive Area CFAR • Because the distribution of radar cross section of aircraft overlaps the cross section distribution of birds (and other clutter targets), a significant number of these targets will pass mean level CFARs, when they are implemented of an individual range-azimuth-Doppler cell. • Use of Area CFAR techniques can mitigate this problem – See Reference 7 for an illustrative example of one such Area CFAR technique Figure adapted from Karp, reference 7 Target Cross Section (dBsm) -30 -20 -10 0 10 TargetDensityperScan(2dBbins) 0.25 0.20 0.10 0.15 0.05 0.0 Measured Aircraft-Bird Density ASR-8 Burlington, VT
Radar Systems Course 12 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Initiation • A track is usually initiated after the detection of three on more scans of the radar to prevent excessive false track from being established. – These detections are checked consistent motion along a reasonable trajectory and velocity profile of targets of interest • A clutter map may be used to prevent track initiation in areas of strong clutter echoes not suppressed by the Doppler processing – The clutter map may also keep track of large bird echoes, so as to not be reinitiating track on them, repeatedly • Military aircraft often have requirements that demand quicker track initiation than civil ATC radars (4–12 sec scans rates) – High speed, low altitude targets that break the horizon at short range – Issue mitigated by use of phased arrays with variable update rates • Track initiation in a dense clutter environment can be quite demanding on computer software and hardware resources
Radar Systems Course 13 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society • First, new detections are correlation with the predicted position that existing tracks be • Is its position consistent with the velocity of the established track – The track gate should be Large enough to be consistent with noise estimates Small enough to minimize false alarms in the gate • Maneuvers of the target pose a more difficult problem • False Alarms may be in the tracking gate – Track Bifurcation (two tracks) – One will probably end soon Track Association Detections from an existing track New Detection Track gate Predicted Position Non-Maneuvering Track gate Maneuvering Track gate Predicted Position
Radar Systems Course 14 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Association • If target association is successful, the track files are updated with the new target detection data • Two tracks may cross, confusing the track association problem – This problem is usually solved using multiple hypothesis testing – I simple words, which paring of the detections in the overlapping gates are more probable Fitted Trajectory of track 1 Fitted Trajectory of track 2 Criteria Proximity of detection to predicted position Similarity of RCS of detections to those of tracks Similarity of velocity of detections to those of tracks Similarity of target lengths of detection to those of tracks
Radar Systems Course 15 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society • On the basis of a series of past detections, the tracker makes a smoothed (filtered) estimate of the target’s present position and velocity; and using this estimate, it predicts the location of the target on the next scan • The simplest method is an tracker – It calculates the present smoothed target position and velocity thus (in one dimension) : • Thus, on the N th scan the predicted position is given by: Track Smoothing (Filtering) & Prediction β−α ( )PNNPNN xxxx −α+= ( )PNN1NN xx T xx − β += − && = measured position on the N th scan = predicted position on N th scan = time between scans = position smoothing parameter = velocity smoothing parameter Nx T PNx α β Txxx NN)1N(P &+=+ Smoothed Position Smoothed Velocity
Radar Systems Course 16 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Smoothing (Filtering) & Prediction • When and are 0, the smoothed tracker information – Current target data is not included – The smoothed data is more important calculating the predicted position, the closer they are to 0 • When and are 1, there is no smoothing of the data – The current measured data is more important calculating the predicted position, the closer they are to 0 • If acceleration of the target is present these filtering and prediction equations can be extended to produce an filter based upon these equations of motion: ( )PNNPNN xxxx −α+= ( )PNN1NN xx T xx − β += − && α α β β γβα N1NNN1N 2 NNN1N xxTxxx 2 T xTxxx &&&&&&&&& =+=++= +++
Radar Systems Course 17 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Transient Errors Caused by Maneuvering of the Target Filter -2 -1 0 1 2 3 4 5 6 + + + + + + + nx n -2 -1 0 1 2 3 4 5 6 + + + + ++ + n Transient Error resulting from abrupt step change in velocity when using the filter nx + ++ = True target trajectory nx PNx = Predicted target trajectory PNx βα βα Figure adapted from Brookner, reference 6
Radar Systems Course 18 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Maneuvering Target Issues • If the previous ramp trajectory is a reasonable approximation to an angular change in direction of the target, then for an tracker, the steady state noise error is minimized if : – The optimum value of and is determined by the bandwidth (range resolution) and other radar system factors, such as target maneuvering capability • One can select and based on a least squares fit to the track data – This approach yields • In another approach, the parameters and can be made to adaptively vary as the target does or does not maneuver βα α− α =β 2 2 Benedict – Bordner Equation )1n(n 6 + =β β )1n(n )1n2(2 + − =α α n = Number of Observations 2n > α β α β
Radar Systems Course 19 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Examples of Maneuvering Targets • Bar-Shalom has noted that a commercial airliner , which can perform a 90° maneuver in 30 seconds ( turn rate 3°/sec) will detect the aircraft 3 times. – That is a very difficult task for a typical long range ATC radar – Fortunately, away from the terminal area they rarely, if ever take such drastic turns – In the terminal area (range 60 nmi), they are seen by ASR with a 4.8 sec data rate • On the other hand, military fighter aircraft can execute up to 5 g turns! – As one would expect, military radars have much higher track revisit times Many of these are phased array radars with appropriately high track update rates
Radar Systems Course 20 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Kalman Filter • As opposed to the filter , the Kalman filter intrinsically is capable of dealing with maneuvering targets • Kalman filter inputs: – Model of measurement error – Target trajectory and its errors (equations of motion) – Trajectory uncertainties Atmospheric turbulence, Unexpected maneuvers, etc. • The Kalman filter reduces to an filter for Gaussian white noise and a constant velocity trajectory and are computed sequentially by the Kalman filter process • Skolnik notes in Reference 1, “The Kalmen filter has much better performance than the tracker since it utilizes more information” – Other have noted the same judgments (see reference 1) – It should be noted that the Kalman filter is much more computationally intensive that the filter βα βα α β βα βα
Radar Systems Course 21 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Two State Kalman Filter (in Brookner’s notation) [ ]xy T xx * 1n,nn n* -1nn, * n,1n −+ − α += && [ ]* -1nn,nn * n1,n * -1nn, * n1,n xyxTxx −β++= ++ & Solution is an Filter with Weights, and , that Vary with n (scan number) βα nβnα See Brookner, reference 6
Radar Systems Course 22 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Filter ( Filter) in Brookner’s Notation [ ]xy T Txxx * 1n,nn n* n,1n * -1nn, * 1n,n −++ − β ++= &&&& [ ]* -1nn,nn 2 * n,1n * nn, * -1nn, * n1,n xy 2 T xTxxx −α+++= ++ &&& [ ]xy T 2 xx * 1n,nn2 n* -1nn, * n,1n −+ − γ += &&&& Where: [ ]xy T xx * 1n,nn n* -1nn, * n,n −− β += && See Brookner, reference 6 γβα
Radar Systems Course 23 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Filtered estimate of position LOW SNR Combining Predictions with Observations Observed position from new measurement Predicted position based on past measurements Range Cross- Range True Target Position Cloud of “uncertainty” for new measurement Cloud of “uncertainty” for new prediction True Target Path Courtesy of MIT Lincoln Laboratory Used with Permission
Radar Systems Course 24 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Predicted position based on past measurements True Target Position Combining Predictions with Observations Range Cross- Range True Target Path HIGH SNR Observed position from new measurement Cloud of “uncertainty” for new prediction Cloud of “uncertainty” for new measurement Filtered estimate of position Courtesy of MIT Lincoln Laboratory Used with Permission
Radar Systems Course 25 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Files and Track Updating • A master Track File is kept of all track that have been initiated • The Track File usually contains the following information – Position and amplitude, and Doppler velocity (if available) measurements of each detection and their time tag – Smoothed position and velocity information – Predicted position and velocity information at the time of the next track update – Track firmness (a measure of detection quality) • After a detection is associated with a track, the Track File is updated
Radar Systems Course 26 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Termination • Track termination – If data from target is missing on a scan of radar, track may be “coasted” – If data from target missing for a number of scans, the track is terminated • The criterion for terminating a track varies for different types of radars – Skolnik (see reference 1) suggests that, if three scans are used to establish a track, then five consecutive missed detections is a reasonable criterion for track termination” • For a “high value target”, such as a sea skimming missile heading towards a ship, different approaches would probably be taken
Radar Systems Course 27 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Tracking on a Angular Sector by Sector • Correlation of new detections with established tracks and subsequent track updating can be accomplished on a sector by sector basis 4 86 10 12 Initiation Tentative Track Firm Track Clutter Points Radar Position Adapted from Trunk, in Skolnik, Reference 1 Sectors 4 to 12 are shown above 1. Radar is collecting data in sector 12 2. Detections from sectors 9 to 11 are being inspected to ascertain if they are clutter false alarm areas stored in clutter map. If so they are deleted 3. Track association is performed on detections from sectors 7 to 9 and are used to preferentially update firm tracks, if association is positive 4. Tentative tracks are secondarily updated (sector 6) if the data gives positive association 5. Tentative tracks (sector 4) are established on remaining detections and If appropriate tracks are terminated
Radar Systems Course 28 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Tracking with Phased Array Radar • Tracking techniques are similar to automatic detection and tracking just described • Advantage of phased array – Flexible track update rate (higher or lower, as required) as opposed to mechanically scanned radars with constant antenna rotation rate – Electronic beam steering enables simultaneously tracking of multiple targets separated by many beamwidths • There is no closed loop feedback control of the radar beam – Computer controls the radar beam and track update rate Courtesy of Dept of Defense. Courtesy of US Navy. Courtesy of Raytheon. Used with permission. Courtesy of US Air Force.
Radar Systems Course 29 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
Radar Systems Course 30 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Background • Most trackers assume that false detection and missed detections on a target in track are uncorrelated, random and noise-like – The theory for predicting the performance with correlated noise and /or missed detections is more complex – The phenomena, which cause these effects, is difficult to predict • Often this assumption is incorrect and leads to poor tracking performance – In many cases these false detections and missed detections are correlated both spatially and temporally
Radar Systems Course 31 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Phenomena Causing Correlated False Detections • Birds (Swarms of bats and insects, too!) – Real targets whose cross section distribution overlap that of a civil ATC target environment (see Viewgraph 11 of this lecture) – Even more so for military aircraft with RCS reduction techniques applied) • Sea spikes (See clutter lecture) • Rain clutter – When ineffective non-coherent integration and techniques are employed in the radar – Adaptive thresholding edge effects – Use of too few pulses in the coherent Doppler filter processing At least 8 are necessary in low PRF ATC radars Result is high Doppler sidelobes and thus poor rain rejection • Ground Clutter – In regions, where the radar has insufficient A/D dynamic range, to allow effective clutter suppression
Radar Systems Course 32 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Phenomena Causing Correlated Missed Detections • Blind Speed effects • Insufficiently high Doppler velocity filter sidelobes to reject rain (see previous viewgraph) in conjunction with a good CFAR • Good CFAR thresholding in regions of significant ground clutter break through
Radar Systems Course 33 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
Radar Systems Course 34 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Before Detect Techniques • Probability of detection may be improved by non-coherently integrating the radar echoes over multiple scans of the radar – Used for weak targets or to extend detection range • Long integration times implies target may traverse many resolution cells during the integration time • Since target trajectory usually not known beforehand, integration must be performed assuming all possible trajectories – Computationally intensive problem • A correct trajectory is one that provides a realistic speed and direction for the type of target being observed – Unexpected maneuvers of target can limit the viability of this technique
Radar Systems Course 35 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Before Detect Techniques • The target must be tracked before it is detected – Also called: Retrospective detection, long term integration • N Scans of data are for all reasonable trajectory hypotheses – This type of straightforward exhaustive search can become computational impractical for very large values of N – Dynamic programming techniques have been developed , which can reduce the computational load by at least five orders of magnitude • Higher single scan probability of false alarm can be tolerated – PFA= 10-3 rather than 10-5 or 10-6 • Use of track before detect techniques requires : – Significantly increased data processing capability – Longer observation time Implying longer delay time before track initiation is declared Courtesy of MIT Lincoln Laboratory Used with Permission
Radar Systems Course 36 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
Radar Systems Course 37 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Integrated Multiple Radar Tracking • Advantages – Improved tracking – Greater data rate than a single radar – Less vulnerability to electronic counter measures – Fewer overall missed detections Fewer gaps in Coverage Filling in of multipath nulls Detection at multiple look angles, reduces chance of a RCS null • Co-located radars vs. multiple radar sites – Co-located radars can operate at different frequencies – Multiple sites Tracks from each radar must be correlated with the others radars This issue has been a long standing problem Implementation of GPS at each radar site (for accurate site geographic registration) has greatly reduced this issues significance
Radar Systems Course 38 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Summary – Part 2 • The multi-target tracking function, by which radar detections from successive scans are associated and formed into tracks, was describe • The various parts of this tracking process were presented, among them: – Track Initiation – Association of detections with tracks – Track smoothing (filtering) and prediction filter and Kalman filter – Track updating and termination • The effect on tracking of correlated missed detections and false alarms was presented • The benefits of multi-radar netting were discussed βα
Radar Systems Course 39 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Homework Problems • From Skolnik, Reference 1 – Problems 4.17, 4.18, and 4.19 • Brookner, Reference 6 – Problems 1.2.1-1, 1.2.1-2, and 1.2.1-3 – Note an α – β filter in notation of the lecture is a “f-g” filter in Broonker’s notation His problems are at the end of his book, not at the end of each chapter
Radar Systems Course 40 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society References 1. Skolnik, M., Introduction to Radar Systems, McGraw-Hill, New York, 3rd Ed., 2001 2. Barton, D. K., Modern Radar System Analysis, Norwood, Mass., Artech House, 1988 3. Skolnik, M., Editor in Chief, Radar Handbook, New York, McGraw-Hill, 3rd Ed., 2008 4. Skolnik, M., Editor in Chief, Radar Handbook, New York, McGraw-Hill, 2nd Ed., 1990 5. O’Donnell, R. M., "The Effect of Correlated Missed Detections and Correlated False Alarms on the Performance of an Automated Radar Tracking System“ , IEEE EASCON '77 Proceedings, Washington D.C., October 1977. 6. Brookner, E., Tracking and Kalman Filtering Made Easy, New York, Wiley, 1998 7. Karp, D. Moving Target Detector Mod II Summary Report, Project Report ATC 96, MIT Lincoln Laboratory. 1981
Radar Systems Course 41 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Acknowledgements • Dr Katherine A. Rink • Dr Eli Brookner, Raytheon Co.

More Related Content

PDF
Radar 2009 a 15 parameter estimation and tracking part 1
byForward2025
 
PDF
Introduction Radar signal processing.pdf
byRaoni13
 
PDF
Radar 2009 a 12 clutter rejection basics and mti
byForward2025
 
PDF
Radar 2009 a 11 waveforms and pulse compression
byForward2025
 
PPSX
Chapter 3- pulsed radar system and MTI
byRima Assaf
 
PPTX
Cw and fm cw radar
byVijendrasingh Rathor
 
PPT
Pulse Doppler Radar
byAkhilesh Arora
 
PPT
Principle of FMCW radar
bytobiasotto
 
Radar 2009 a 15 parameter estimation and tracking part 1
byForward2025
 
Introduction Radar signal processing.pdf
byRaoni13
 
Radar 2009 a 12 clutter rejection basics and mti
byForward2025
 
Radar 2009 a 11 waveforms and pulse compression
byForward2025
 
Chapter 3- pulsed radar system and MTI
byRima Assaf
 
Cw and fm cw radar
byVijendrasingh Rathor
 
Pulse Doppler Radar
byAkhilesh Arora
 
Principle of FMCW radar
bytobiasotto
 

What's hot

PDF
Radar 2009 a 7 radar cross section 1
byForward2025
 
PDF
Radar 2009 a 9 antennas 2
byForward2025
 
PDF
Radar 2009 a 10 radar clutter.2pdf
byForward2025
 
PDF
Radar 2009 a 4 radar equation
byForward2025
 
PPTX
Prediction of range performance
bybhupender rawat
 
PDF
Radar 2009 a 19 electronic counter measures
byForward2025
 
PDF
Introduction to Radar System & Component Tests
byRohde & Schwarz North America
 
PDF
Tracking Radar
byEdwin Muriyaden Wilson
 
PPT
Radar Basics
byAziz Zoaib
 
PPTX
RADAR
byAMAN SRIVASTAVA
 
PPT
radar-principles
byjhcid
 
PPT
Components of a Pulse Radar System
byÜlger Ahmet
 
PPTX
Introduction to equalization
byHarshit Srivastava
 
PDF
Link budget calculation
bysitimunirah88
 
PDF
Multi-Funtion Phased Array Radar
byMistral Solutions
 
PPTX
Study of Radar System PPT
byAtul Sharma
 
PPTX
4th UNIT Microwave Engineering.pptx
byKeerthiniKurapati
 
PPT
5 pulse compression waveform
bySolo Hermelin
 
PDF
Propagation Effects for Radar&Comm Systems
byJim Jenkins
 
PPT
propagation-and-antenna
byATTO RATHORE
 
Radar 2009 a 7 radar cross section 1
byForward2025
 
Radar 2009 a 9 antennas 2
byForward2025
 
Radar 2009 a 10 radar clutter.2pdf
byForward2025
 
Radar 2009 a 4 radar equation
byForward2025
 
Prediction of range performance
bybhupender rawat
 
Radar 2009 a 19 electronic counter measures
byForward2025
 
Introduction to Radar System & Component Tests
byRohde & Schwarz North America
 
Tracking Radar
byEdwin Muriyaden Wilson
 
Radar Basics
byAziz Zoaib
 
RADAR
byAMAN SRIVASTAVA
 
radar-principles
byjhcid
 
Components of a Pulse Radar System
byÜlger Ahmet
 
Introduction to equalization
byHarshit Srivastava
 
Link budget calculation
bysitimunirah88
 
Multi-Funtion Phased Array Radar
byMistral Solutions
 
Study of Radar System PPT
byAtul Sharma
 
4th UNIT Microwave Engineering.pptx
byKeerthiniKurapati
 
5 pulse compression waveform
bySolo Hermelin
 
Propagation Effects for Radar&Comm Systems
byJim Jenkins
 
propagation-and-antenna
byATTO RATHORE
 

Viewers also liked

PDF
Radar 2009 a 7 radar cross section 2
byForward2025
 
PDF
Radar 2009 a 14 airborne pulse doppler radar
byForward2025
 
PDF
Radar 2009 a 13 clutter rejection doppler filtering
byForward2025
 
PDF
Radar 2009 a 5 propagation effects
byForward2025
 
PDF
Radar 2009 a 6 detection of signals in noise
byForward2025
 
PDF
Radar 2009 a 17 transmitters and receivers
byForward2025
 
PDF
Radar 2009 a 8 antennas 1
byForward2025
 
PPTX
A Tutorial on Radar System Engineering
byTBSS Group
 
PPT
Target tracking
byabdulrehmanali
 
PDF
Radar 2009 a 18 synthetic aperture radar
byForward2025
 
PDF
Radar 2009 a 10 radar clutter1
byForward2025
 
PPT
Monopulse tracking radar
byAshok Selsan
 
PDF
Radar 2009 a 3 review of signals systems and dsp
byForward2025
 
PDF
Radar 2009 a 1 introduction
byForward2025
 
DOCX
A PROJECT REPORT
byShruti Mishra
 
PDF
Radar 2009 a 2 review of electromagnetism3
byForward2025
 
PDF
Radar 2009 a 0 prelude
byForward2025
 
PDF
A digital revisitation_of_analog_beamforming_techniques - aiaaicssc2013_lisi
byMarco Lisi
 
PDF
Direction of Arrival Estimation using a Sparse Linear Antenna Array
byIDES Editor
 
PDF
A NOVEL SNAPSHOT BASED APPROACH FOR DIRECTION OF ARRIVIAL ESTIMATION WITH LEA...
byProfessor at RYM Engineering College, Ballari
 
Radar 2009 a 7 radar cross section 2
byForward2025
 
Radar 2009 a 14 airborne pulse doppler radar
byForward2025
 
Radar 2009 a 13 clutter rejection doppler filtering
byForward2025
 
Radar 2009 a 5 propagation effects
byForward2025
 
Radar 2009 a 6 detection of signals in noise
byForward2025
 
Radar 2009 a 17 transmitters and receivers
byForward2025
 
Radar 2009 a 8 antennas 1
byForward2025
 
A Tutorial on Radar System Engineering
byTBSS Group
 
Target tracking
byabdulrehmanali
 
Radar 2009 a 18 synthetic aperture radar
byForward2025
 
Radar 2009 a 10 radar clutter1
byForward2025
 
Monopulse tracking radar
byAshok Selsan
 
Radar 2009 a 3 review of signals systems and dsp
byForward2025
 
Radar 2009 a 1 introduction
byForward2025
 
A PROJECT REPORT
byShruti Mishra
 
Radar 2009 a 2 review of electromagnetism3
byForward2025
 
Radar 2009 a 0 prelude
byForward2025
 
A digital revisitation_of_analog_beamforming_techniques - aiaaicssc2013_lisi
byMarco Lisi
 
Direction of Arrival Estimation using a Sparse Linear Antenna Array
byIDES Editor
 
A NOVEL SNAPSHOT BASED APPROACH FOR DIRECTION OF ARRIVIAL ESTIMATION WITH LEA...
byProfessor at RYM Engineering College, Ballari
 

Similar to Radar 2009 a 16 parameter estimation and tracking part2

PPTX
Radar fundamentals
byAli Sufyan
 
PDF
AESA Airborne Radar Theory and Operations Technical Training Course Sampler
byJim Jenkins
 
PDF
14. doppler radar and mti 2014
byLohith Kumar
 
PDF
IARE_RS_PPT_0.pdf
byamitkumar473416
 
PDF
Radar target detection simulation
byIJERA Editor
 
PPTX
Radar signal process(Radar)
bykhalidmorsalin
 
PDF
MTT Data Association
byEngin Gul
 
PPT
TargetTracking[1].ppt random finite set presentation
byZvonkoRadosavljevic
 
PPT
Radar Principles & Systems.ppt
bymancangkul1
 
PPTX
UNjjjjjjjjjjjjjjjjjjjjjjjjjoooIT - 4.pptx
byobulareddydinesh
 
PPT
Radar and Navigational Aids Radio Frequency Data Communications Course Materials
byRAVIKUMAR Digital Signal Processing
 
PDF
introduction and classification to radar.pdf
byassdali110202
 
PDF
Application Of Digital Signal Processing In Radar Signals
byRichard Hogue
 
PPSX
Chapter 1-generality
byRima Assaf
 
PPT
Radar
byPankaj Jagadale
 
PDF
Target Detection in Noise. How best to detect.
bySamOyelowo
 
DOC
AESARadarClassSynopsisV2.4
byRobert Phillips
 
PPT
Intro to Multitarget Tracking for CURVE
bychenhm
 
DOCX
PROJECT REPORT1 (1) new
byAnkita Badal
 
PDF
Hm3114401445
byIJERA Editor
 
Radar fundamentals
byAli Sufyan
 
AESA Airborne Radar Theory and Operations Technical Training Course Sampler
byJim Jenkins
 
14. doppler radar and mti 2014
byLohith Kumar
 
IARE_RS_PPT_0.pdf
byamitkumar473416
 
Radar target detection simulation
byIJERA Editor
 
Radar signal process(Radar)
bykhalidmorsalin
 
MTT Data Association
byEngin Gul
 
TargetTracking[1].ppt random finite set presentation
byZvonkoRadosavljevic
 
Radar Principles & Systems.ppt
bymancangkul1
 
UNjjjjjjjjjjjjjjjjjjjjjjjjjoooIT - 4.pptx
byobulareddydinesh
 
Radar and Navigational Aids Radio Frequency Data Communications Course Materials
byRAVIKUMAR Digital Signal Processing
 
introduction and classification to radar.pdf
byassdali110202
 
Application Of Digital Signal Processing In Radar Signals
byRichard Hogue
 
Chapter 1-generality
byRima Assaf
 
Radar
byPankaj Jagadale
 
Target Detection in Noise. How best to detect.
bySamOyelowo
 
AESARadarClassSynopsisV2.4
byRobert Phillips
 
Intro to Multitarget Tracking for CURVE
bychenhm
 
PROJECT REPORT1 (1) new
byAnkita Badal
 
Hm3114401445
byIJERA Editor
 

More from Forward2025

PPT
Lecture 3
byForward2025
 
PPT
Lecture 10
byForward2025
 
PPT
Lecture 7
byForward2025
 
PPT
Lecture 5
byForward2025
 
PPT
Lecture 8
byForward2025
 
PPT
Lecture 2
byForward2025
 
PPT
Lecture 16
byForward2025
 
PPT
Lecture 14
byForward2025
 
PPT
Lecture 15
byForward2025
 
PPT
Lecture 13
byForward2025
 
PPT
Lecture 6
byForward2025
 
PPT
Lecture 18
byForward2025
 
PPT
Lecture 9
byForward2025
 
PPT
Lecture 12
byForward2025
 
PPT
Lecture 1
byForward2025
 
PPT
Lecture 11
byForward2025
 
PPT
Lecture 17
byForward2025
 
PPT
Lecture 4
byForward2025
 
Lecture 3
byForward2025
 
Lecture 10
byForward2025
 
Lecture 7
byForward2025
 
Lecture 5
byForward2025
 
Lecture 8
byForward2025
 
Lecture 2
byForward2025
 
Lecture 16
byForward2025
 
Lecture 14
byForward2025
 
Lecture 15
byForward2025
 
Lecture 13
byForward2025
 
Lecture 6
byForward2025
 
Lecture 18
byForward2025
 
Lecture 9
byForward2025
 
Lecture 12
byForward2025
 
Lecture 1
byForward2025
 
Lecture 11
byForward2025
 
Lecture 17
byForward2025
 
Lecture 4
byForward2025
 

Recently uploaded

PDF
Hybrid Anomaly Detection Mechanism for IOT Networks
byIJCNCJournal
 
PDF
PROPEX-RAG: Prompt-Driven GraphRAG for Multi-Hop QA
byapurvakarne
 
PDF
Advanced Intrusion Detection and Classification using Transfer Learning with ...
byIJCNCJournal
 
PDF
Structural Conservation Appraisal of Indian Monuments Preserving India’s Arch...
byAman Kumar Singh
 
PPTX
Power point presentation on introduction of software engineering
bys6050748
 
PDF
Paralleling of Alternators - First Edition - 2022
byMahmoud Moghtaderi
 
PPTX
22PEOIT4C Session 7 A searching algorithm.pptx
bymailmegokilavani
 
PPTX
Introduction Blockchains and Smart Contracts
bybobinson
 
PPTX
Optimizing Plant Maintenance — Key Elements of a Successful Maintenance Plan ...
byMaintWiz Technologies Private Limited
 
PPTX
Track & Monitor Preventive Maintenance — Best Practices with MaintWiz CMMS
byMaintWiz Technologies Private Limited
 
PPTX
Revolutionizing Facilities Management with MaintWiz — AI CMMS for Smart FMaaS
byMaintWiz Technologies Private Limited
 
PPTX
A7383 3D Printing UNIT-III : 3D printing techniques
byVishnuVardhan909561
 
PPTX
How to Create an Effective Monthly Preventive Maintenance Plan
byMaintWiz Technologies Private Limited
 
PPTX
Optimizing Operations: Key Elements of a Successful Plant Maintenance Plan — ...
byMaintWiz Technologies Private Limited
 
PDF
Nostr : A protocol for freedom of speech
byEmre YILMAZ
 
PDF
TechSprint: Innovate for Impact Hackathon
byDeepakkumarSingh415123
 
PPTX
infosecurity & Cyber Sec 2025 September for deep.pptx
bymsrehan
 
PDF
3rd International Conference on AI & Civil Engineering (AICiViL 2025)
byieijjournal
 
PDF
AI-Driven CTI for Business: Emerging Threats, Attack Strategies, and Defensiv...
byAIRCC Publishing Corporation
 
DOCX
Buy Facebook Ads Accounts_ Boost Your Professional ....docx
bytopusapro.com
 
Hybrid Anomaly Detection Mechanism for IOT Networks
byIJCNCJournal
 
PROPEX-RAG: Prompt-Driven GraphRAG for Multi-Hop QA
byapurvakarne
 
Advanced Intrusion Detection and Classification using Transfer Learning with ...
byIJCNCJournal
 
Structural Conservation Appraisal of Indian Monuments Preserving India’s Arch...
byAman Kumar Singh
 
Power point presentation on introduction of software engineering
bys6050748
 
Paralleling of Alternators - First Edition - 2022
byMahmoud Moghtaderi
 
22PEOIT4C Session 7 A searching algorithm.pptx
bymailmegokilavani
 
Introduction Blockchains and Smart Contracts
bybobinson
 
Optimizing Plant Maintenance — Key Elements of a Successful Maintenance Plan ...
byMaintWiz Technologies Private Limited
 
Track & Monitor Preventive Maintenance — Best Practices with MaintWiz CMMS
byMaintWiz Technologies Private Limited
 
Revolutionizing Facilities Management with MaintWiz — AI CMMS for Smart FMaaS
byMaintWiz Technologies Private Limited
 
A7383 3D Printing UNIT-III : 3D printing techniques
byVishnuVardhan909561
 
How to Create an Effective Monthly Preventive Maintenance Plan
byMaintWiz Technologies Private Limited
 
Optimizing Operations: Key Elements of a Successful Plant Maintenance Plan — ...
byMaintWiz Technologies Private Limited
 
Nostr : A protocol for freedom of speech
byEmre YILMAZ
 
TechSprint: Innovate for Impact Hackathon
byDeepakkumarSingh415123
 
infosecurity & Cyber Sec 2025 September for deep.pptx
bymsrehan
 
3rd International Conference on AI & Civil Engineering (AICiViL 2025)
byieijjournal
 
AI-Driven CTI for Business: Emerging Threats, Attack Strategies, and Defensiv...
byAIRCC Publishing Corporation
 
Buy Facebook Ads Accounts_ Boost Your Professional ....docx
bytopusapro.com
 
In this document
Powered by AI

Introduces the IEEE NH Section, course on radar systems, focusing on Parameter Estimation and Tracking, presented by Dr. Robert M. O’Donnell.

Describes components of a radar system with emphasis on pulse compression, clutter rejection, and tracking data.

Outlines key topics of multiple target tracking including tracking process, correlated detections, and summary.

Shows various radar systems like PATRIOT and AWACS, crediting US Air Force and other organizations.

Details about the tracking process, including track initiation, association, maintenance, and consolidation across various operational circumstances.

Outlines key topics of multiple target tracking including tracking process, correlated detections, and summary.

Details about the tracking process, including track initiation, association, maintenance, and consolidation across various operational circumstances.

Addresses challenges with tracking maneuvering targets, illustrates issues like correlated detections, and emphasizes adaptive techniques.

Discusses methodologies like detection consolidation, Kalman filtering, and track-before-detect approaches in radar systems.

Addresses challenges with tracking maneuvering targets, illustrates issues like correlated detections, and emphasizes adaptive techniques.

Discusses methodologies like detection consolidation, Kalman filtering, and track-before-detect approaches in radar systems.

Details about the tracking process, including track initiation, association, maintenance, and consolidation across various operational circumstances.

Discusses methodologies like detection consolidation, Kalman filtering, and track-before-detect approaches in radar systems.

Outlines key topics of multiple target tracking including tracking process, correlated detections, and summary.

Discusses limitations in tracking performance due to environmental factors and the advantages of integrated multiple radar tracking.

Addresses challenges with tracking maneuvering targets, illustrates issues like correlated detections, and emphasizes adaptive techniques.

Outlines key topics of multiple target tracking including tracking process, correlated detections, and summary.

Discusses methodologies like detection consolidation, Kalman filtering, and track-before-detect approaches in radar systems.

Discusses limitations in tracking performance due to environmental factors and the advantages of integrated multiple radar tracking.Summarizes key concepts from the tracking process and assigns homework problems based on references.

Lists references used in the presentation and acknowledges contributions from individuals in the radar systems field.

Radar 2009 a 16 parameter estimation and tracking part2

  • 1.
    IEEE New HampshireSection Radar Systems Course 1 Tracking 1/1/2010 IEEE AES Society Radar Systems Engineering Lecture 16 Parameter Estimation and Tracking Part 2 Dr. Robert M. O’Donnell IEEE New Hampshire Section Guest Lecturer
  • 2.
    Radar Systems Course2 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Pulse Compression Receiver Clutter Rejection (Doppler Filtering) A / D Converter Block Diagram of Radar System Antenna Propagation Medium Target Radar Cross Section Transmitter General Purpose Computer Tracking Data Recording Parameter Estimation Waveform Generation Detection Power Amplifier T / R Switch Signal Processor Computer Thresholding User Displays and Radar Control Photo Image Courtesy of US Air Force
  • 3.
    Radar Systems Course3 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
  • 4.
    Radar Systems Course4 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Multiple Target Tracking Radars Courtesy of USAF Courtesy of FAACourtesy of FAA Courtesy of Raytheon, Used with Permission.Courtesy of NATOCourtesy of NATO ASR-9Patriot AWACSRussian “FLAP LID” S-300 Courtesy of Martin Rosenkrantz, Used with PermissionCourtesy of Martin Rosenkrantz, Used with Permission Courtesy of US Air ForceCourtesy of US Air Force
  • 5.
    Radar Systems Course5 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Radar Tracking Example • Tracker receives new observations every scan – Target observations – False alarms Tracker Input Tracker Output RangeRange Cross-Range • New tracks are initiated • Existing tracks are updated • Obsolete tracks are deleted New Track Existing Track Observations True Target Position Courtesy of MIT Lincoln Laboratory Used with Permission
  • 6.
    Radar Systems Course6 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
  • 7.
    Radar Systems Course7 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track File (Track States) Block Diagram of Tracking Process Detection Threshold Crossings Adaptive Area CFAR Track Termination Track Association ? Detection Consolidation Track Update Track Prediction Track Initiation Radar Scheduling Control Track Update Rate Monitoring For Phased Array Radars New Raw Target Reports Filtered Raw Target Reports Non-associated Reports Existing TracksYes Yes No New Tracks Track Formation To Radar Control Tracks
  • 8.
    Radar Systems Course8 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Simplified Tracking Tasks • Track Association – Associate new observations with existing tracks • Track Initiation – Initiate new tracks on non- associated observations (two or three scans) • Track Maintenance – Update a smoothed (filtered) estimate of the target’s present state – Predict new estimate of target’s state on the next scan • Track Termination – Terminate tracks that are missing new observations for a number of scans Existing Tracks New Tracks New Observations Non-associated Observations Detection Reports: range, range rate, azimuth, elevation, etc. Association PredictionInitiation Update Termination Courtesy of MIT Lincoln Laboratory Used with Permission
  • 9.
    Radar Systems Course9 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Goal of the Tracking Process • Input – Raw detections from the radar Threshold crossings for each range, azimuth, • Output – State vector for each viable target and its predicted state vector at a later time State vector = – Amplitude information – Track “quality” information i.e. track life, etc. • Few, if any false tracks !! t,z,y,x,z,y,x &&&
  • 10.
    Radar Systems Course10 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Detection Consolidation • Often, a target may produce many adjacent threshold crossings • Adjacency in range, angle and Doppler velocity are usually used as the criteria for grouping clusters of detections • The sets of threshold crossings (and their amplitudes) are then used to interpolation to determine a more accurate value of each measured observables – Weighted average – Fit data to the expected angle beam shape (angle), pulse response (range), or Doppler filter shape (Doppler velocity) To radar 1/16 nmi Target Detections From 4 CPI’s 1Beamwidth -60 kts 0 60 kts dB 0 -80 -60 -40 -20 Noise Level -60 kts 0 60 kts dB 0 -20 -40 -60 -80 Noise Level Commercial Airliner Two Engine Propeller Aircraft Aircraft Doppler Spectra
  • 11.
    Radar Systems Course11 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Adaptive Area CFAR • Because the distribution of radar cross section of aircraft overlaps the cross section distribution of birds (and other clutter targets), a significant number of these targets will pass mean level CFARs, when they are implemented of an individual range-azimuth-Doppler cell. • Use of Area CFAR techniques can mitigate this problem – See Reference 7 for an illustrative example of one such Area CFAR technique Figure adapted from Karp, reference 7 Target Cross Section (dBsm) -30 -20 -10 0 10 TargetDensityperScan(2dBbins) 0.25 0.20 0.10 0.15 0.05 0.0 Measured Aircraft-Bird Density ASR-8 Burlington, VT
  • 12.
    Radar Systems Course12 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Initiation • A track is usually initiated after the detection of three on more scans of the radar to prevent excessive false track from being established. – These detections are checked consistent motion along a reasonable trajectory and velocity profile of targets of interest • A clutter map may be used to prevent track initiation in areas of strong clutter echoes not suppressed by the Doppler processing – The clutter map may also keep track of large bird echoes, so as to not be reinitiating track on them, repeatedly • Military aircraft often have requirements that demand quicker track initiation than civil ATC radars (4–12 sec scans rates) – High speed, low altitude targets that break the horizon at short range – Issue mitigated by use of phased arrays with variable update rates • Track initiation in a dense clutter environment can be quite demanding on computer software and hardware resources
  • 13.
    Radar Systems Course13 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society • First, new detections are correlation with the predicted position that existing tracks be • Is its position consistent with the velocity of the established track – The track gate should be Large enough to be consistent with noise estimates Small enough to minimize false alarms in the gate • Maneuvers of the target pose a more difficult problem • False Alarms may be in the tracking gate – Track Bifurcation (two tracks) – One will probably end soon Track Association Detections from an existing track New Detection Track gate Predicted Position Non-Maneuvering Track gate Maneuvering Track gate Predicted Position
  • 14.
    Radar Systems Course14 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Association • If target association is successful, the track files are updated with the new target detection data • Two tracks may cross, confusing the track association problem – This problem is usually solved using multiple hypothesis testing – I simple words, which paring of the detections in the overlapping gates are more probable Fitted Trajectory of track 1 Fitted Trajectory of track 2 Criteria Proximity of detection to predicted position Similarity of RCS of detections to those of tracks Similarity of velocity of detections to those of tracks Similarity of target lengths of detection to those of tracks
  • 15.
    Radar Systems Course15 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society • On the basis of a series of past detections, the tracker makes a smoothed (filtered) estimate of the target’s present position and velocity; and using this estimate, it predicts the location of the target on the next scan • The simplest method is an tracker – It calculates the present smoothed target position and velocity thus (in one dimension) : • Thus, on the N th scan the predicted position is given by: Track Smoothing (Filtering) & Prediction β−α ( )PNNPNN xxxx −α+= ( )PNN1NN xx T xx − β += − && = measured position on the N th scan = predicted position on N th scan = time between scans = position smoothing parameter = velocity smoothing parameter Nx T PNx α β Txxx NN)1N(P &+=+ Smoothed Position Smoothed Velocity
  • 16.
    Radar Systems Course16 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Smoothing (Filtering) & Prediction • When and are 0, the smoothed tracker information – Current target data is not included – The smoothed data is more important calculating the predicted position, the closer they are to 0 • When and are 1, there is no smoothing of the data – The current measured data is more important calculating the predicted position, the closer they are to 0 • If acceleration of the target is present these filtering and prediction equations can be extended to produce an filter based upon these equations of motion: ( )PNNPNN xxxx −α+= ( )PNN1NN xx T xx − β += − && α α β β γβα N1NNN1N 2 NNN1N xxTxxx 2 T xTxxx &&&&&&&&& =+=++= +++
  • 17.
    Radar Systems Course17 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Transient Errors Caused by Maneuvering of the Target Filter -2 -1 0 1 2 3 4 5 6 + + + + + + + nx n -2 -1 0 1 2 3 4 5 6 + + + + ++ + n Transient Error resulting from abrupt step change in velocity when using the filter nx + ++ = True target trajectory nx PNx = Predicted target trajectory PNx βα βα Figure adapted from Brookner, reference 6
  • 18.
    Radar Systems Course18 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Maneuvering Target Issues • If the previous ramp trajectory is a reasonable approximation to an angular change in direction of the target, then for an tracker, the steady state noise error is minimized if : – The optimum value of and is determined by the bandwidth (range resolution) and other radar system factors, such as target maneuvering capability • One can select and based on a least squares fit to the track data – This approach yields • In another approach, the parameters and can be made to adaptively vary as the target does or does not maneuver βα α− α =β 2 2 Benedict – Bordner Equation )1n(n 6 + =β β )1n(n )1n2(2 + − =α α n = Number of Observations 2n > α β α β
  • 19.
    Radar Systems Course19 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Examples of Maneuvering Targets • Bar-Shalom has noted that a commercial airliner , which can perform a 90° maneuver in 30 seconds ( turn rate 3°/sec) will detect the aircraft 3 times. – That is a very difficult task for a typical long range ATC radar – Fortunately, away from the terminal area they rarely, if ever take such drastic turns – In the terminal area (range 60 nmi), they are seen by ASR with a 4.8 sec data rate • On the other hand, military fighter aircraft can execute up to 5 g turns! – As one would expect, military radars have much higher track revisit times Many of these are phased array radars with appropriately high track update rates
  • 20.
    Radar Systems Course20 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Kalman Filter • As opposed to the filter , the Kalman filter intrinsically is capable of dealing with maneuvering targets • Kalman filter inputs: – Model of measurement error – Target trajectory and its errors (equations of motion) – Trajectory uncertainties Atmospheric turbulence, Unexpected maneuvers, etc. • The Kalman filter reduces to an filter for Gaussian white noise and a constant velocity trajectory and are computed sequentially by the Kalman filter process • Skolnik notes in Reference 1, “The Kalmen filter has much better performance than the tracker since it utilizes more information” – Other have noted the same judgments (see reference 1) – It should be noted that the Kalman filter is much more computationally intensive that the filter βα βα α β βα βα
  • 21.
    Radar Systems Course21 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Two State Kalman Filter (in Brookner’s notation) [ ]xy T xx * 1n,nn n* -1nn, * n,1n −+ − α += && [ ]* -1nn,nn * n1,n * -1nn, * n1,n xyxTxx −β++= ++ & Solution is an Filter with Weights, and , that Vary with n (scan number) βα nβnα See Brookner, reference 6
  • 22.
    Radar Systems Course22 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Filter ( Filter) in Brookner’s Notation [ ]xy T Txxx * 1n,nn n* n,1n * -1nn, * 1n,n −++ − β ++= &&&& [ ]* -1nn,nn 2 * n,1n * nn, * -1nn, * n1,n xy 2 T xTxxx −α+++= ++ &&& [ ]xy T 2 xx * 1n,nn2 n* -1nn, * n,1n −+ − γ += &&&& Where: [ ]xy T xx * 1n,nn n* -1nn, * n,n −− β += && See Brookner, reference 6 γβα
  • 23.
    Radar Systems Course23 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Filtered estimate of position LOW SNR Combining Predictions with Observations Observed position from new measurement Predicted position based on past measurements Range Cross- Range True Target Position Cloud of “uncertainty” for new measurement Cloud of “uncertainty” for new prediction True Target Path Courtesy of MIT Lincoln Laboratory Used with Permission
  • 24.
    Radar Systems Course24 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Predicted position based on past measurements True Target Position Combining Predictions with Observations Range Cross- Range True Target Path HIGH SNR Observed position from new measurement Cloud of “uncertainty” for new prediction Cloud of “uncertainty” for new measurement Filtered estimate of position Courtesy of MIT Lincoln Laboratory Used with Permission
  • 25.
    Radar Systems Course25 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Files and Track Updating • A master Track File is kept of all track that have been initiated • The Track File usually contains the following information – Position and amplitude, and Doppler velocity (if available) measurements of each detection and their time tag – Smoothed position and velocity information – Predicted position and velocity information at the time of the next track update – Track firmness (a measure of detection quality) • After a detection is associated with a track, the Track File is updated
  • 26.
    Radar Systems Course26 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Termination • Track termination – If data from target is missing on a scan of radar, track may be “coasted” – If data from target missing for a number of scans, the track is terminated • The criterion for terminating a track varies for different types of radars – Skolnik (see reference 1) suggests that, if three scans are used to establish a track, then five consecutive missed detections is a reasonable criterion for track termination” • For a “high value target”, such as a sea skimming missile heading towards a ship, different approaches would probably be taken
  • 27.
    Radar Systems Course27 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Tracking on a Angular Sector by Sector • Correlation of new detections with established tracks and subsequent track updating can be accomplished on a sector by sector basis 4 86 10 12 Initiation Tentative Track Firm Track Clutter Points Radar Position Adapted from Trunk, in Skolnik, Reference 1 Sectors 4 to 12 are shown above 1. Radar is collecting data in sector 12 2. Detections from sectors 9 to 11 are being inspected to ascertain if they are clutter false alarm areas stored in clutter map. If so they are deleted 3. Track association is performed on detections from sectors 7 to 9 and are used to preferentially update firm tracks, if association is positive 4. Tentative tracks are secondarily updated (sector 6) if the data gives positive association 5. Tentative tracks (sector 4) are established on remaining detections and If appropriate tracks are terminated
  • 28.
    Radar Systems Course28 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Tracking with Phased Array Radar • Tracking techniques are similar to automatic detection and tracking just described • Advantage of phased array – Flexible track update rate (higher or lower, as required) as opposed to mechanically scanned radars with constant antenna rotation rate – Electronic beam steering enables simultaneously tracking of multiple targets separated by many beamwidths • There is no closed loop feedback control of the radar beam – Computer controls the radar beam and track update rate Courtesy of Dept of Defense. Courtesy of US Navy. Courtesy of Raytheon. Used with permission. Courtesy of US Air Force.
  • 29.
    Radar Systems Course29 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
  • 30.
    Radar Systems Course30 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Background • Most trackers assume that false detection and missed detections on a target in track are uncorrelated, random and noise-like – The theory for predicting the performance with correlated noise and /or missed detections is more complex – The phenomena, which cause these effects, is difficult to predict • Often this assumption is incorrect and leads to poor tracking performance – In many cases these false detections and missed detections are correlated both spatially and temporally
  • 31.
    Radar Systems Course31 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Phenomena Causing Correlated False Detections • Birds (Swarms of bats and insects, too!) – Real targets whose cross section distribution overlap that of a civil ATC target environment (see Viewgraph 11 of this lecture) – Even more so for military aircraft with RCS reduction techniques applied) • Sea spikes (See clutter lecture) • Rain clutter – When ineffective non-coherent integration and techniques are employed in the radar – Adaptive thresholding edge effects – Use of too few pulses in the coherent Doppler filter processing At least 8 are necessary in low PRF ATC radars Result is high Doppler sidelobes and thus poor rain rejection • Ground Clutter – In regions, where the radar has insufficient A/D dynamic range, to allow effective clutter suppression
  • 32.
    Radar Systems Course32 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Phenomena Causing Correlated Missed Detections • Blind Speed effects • Insufficiently high Doppler velocity filter sidelobes to reject rain (see previous viewgraph) in conjunction with a good CFAR • Good CFAR thresholding in regions of significant ground clutter break through
  • 33.
    Radar Systems Course33 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
  • 34.
    Radar Systems Course34 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Before Detect Techniques • Probability of detection may be improved by non-coherently integrating the radar echoes over multiple scans of the radar – Used for weak targets or to extend detection range • Long integration times implies target may traverse many resolution cells during the integration time • Since target trajectory usually not known beforehand, integration must be performed assuming all possible trajectories – Computationally intensive problem • A correct trajectory is one that provides a realistic speed and direction for the type of target being observed – Unexpected maneuvers of target can limit the viability of this technique
  • 35.
    Radar Systems Course35 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Track Before Detect Techniques • The target must be tracked before it is detected – Also called: Retrospective detection, long term integration • N Scans of data are for all reasonable trajectory hypotheses – This type of straightforward exhaustive search can become computational impractical for very large values of N – Dynamic programming techniques have been developed , which can reduce the computational load by at least five orders of magnitude • Higher single scan probability of false alarm can be tolerated – PFA= 10-3 rather than 10-5 or 10-6 • Use of track before detect techniques requires : – Significantly increased data processing capability – Longer observation time Implying longer delay time before track initiation is declared Courtesy of MIT Lincoln Laboratory Used with Permission
  • 36.
    Radar Systems Course36 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Outline - Multiple Target Tracking • Introduction • Tracking Process • Effect of correlated missed detections and correlated false alarms on tracking performance • Track-before-detect techniques • Integrated Multiple Radar Tracking • Summary
  • 37.
    Radar Systems Course37 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Integrated Multiple Radar Tracking • Advantages – Improved tracking – Greater data rate than a single radar – Less vulnerability to electronic counter measures – Fewer overall missed detections Fewer gaps in Coverage Filling in of multipath nulls Detection at multiple look angles, reduces chance of a RCS null • Co-located radars vs. multiple radar sites – Co-located radars can operate at different frequencies – Multiple sites Tracks from each radar must be correlated with the others radars This issue has been a long standing problem Implementation of GPS at each radar site (for accurate site geographic registration) has greatly reduced this issues significance
  • 38.
    Radar Systems Course38 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Summary – Part 2 • The multi-target tracking function, by which radar detections from successive scans are associated and formed into tracks, was describe • The various parts of this tracking process were presented, among them: – Track Initiation – Association of detections with tracks – Track smoothing (filtering) and prediction filter and Kalman filter – Track updating and termination • The effect on tracking of correlated missed detections and false alarms was presented • The benefits of multi-radar netting were discussed βα
  • 39.
    Radar Systems Course39 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Homework Problems • From Skolnik, Reference 1 – Problems 4.17, 4.18, and 4.19 • Brookner, Reference 6 – Problems 1.2.1-1, 1.2.1-2, and 1.2.1-3 – Note an α – β filter in notation of the lecture is a “f-g” filter in Broonker’s notation His problems are at the end of his book, not at the end of each chapter
  • 40.
    Radar Systems Course40 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society References 1. Skolnik, M., Introduction to Radar Systems, McGraw-Hill, New York, 3rd Ed., 2001 2. Barton, D. K., Modern Radar System Analysis, Norwood, Mass., Artech House, 1988 3. Skolnik, M., Editor in Chief, Radar Handbook, New York, McGraw-Hill, 3rd Ed., 2008 4. Skolnik, M., Editor in Chief, Radar Handbook, New York, McGraw-Hill, 2nd Ed., 1990 5. O’Donnell, R. M., "The Effect of Correlated Missed Detections and Correlated False Alarms on the Performance of an Automated Radar Tracking System“ , IEEE EASCON '77 Proceedings, Washington D.C., October 1977. 6. Brookner, E., Tracking and Kalman Filtering Made Easy, New York, Wiley, 1998 7. Karp, D. Moving Target Detector Mod II Summary Report, Project Report ATC 96, MIT Lincoln Laboratory. 1981
  • 41.
    Radar Systems Course41 Tracking 1/1/2010 IEEE New Hampshire Section IEEE AES Society Acknowledgements • Dr Katherine A. Rink • Dr Eli Brookner, Raytheon Co.