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- 1. AESA Airborne Radar Theory and Operations Instructor: Bob Phillips ATI Course Schedule: http://www.ATIcourses.com/schedule.htm ATI's AESA: http://aticourses.com/AESA_Airborne_Radar_Theory.htm
- 2. www.ATIcourses.com Boost Your Skills with On-Site Courses Tailored to Your Needs 349 Berkshire Drive Riva, Maryland 21140 Telephone 1-888-501-2100 / (410) 965-8805 Fax (410) 956-5785 Email: ATI@ATIcourses.com The Applied Technology Institute specializes in training programs for technical professionals. Our courses keep you current in the state-of-the-art technology that is essential to keep your company on the cutting edge in today’s highly competitive marketplace. Since 1984, ATI has earned the trust of training departments nationwide, and has presented on-site training at the major Navy, Air Force and NASA centers, and for a large number of contractors. Our training increases effectiveness and productivity. Learn from the proven best. For a Free On-Site Quote Visit Us At: http://www.ATIcourses.com/free_onsite_quote.asp For Our Current Public Course Schedule Go To: http://www.ATIcourses.com/schedule.htm
- 3. Copyright 2013 R.A. Phillips AESA Airborne Radar Theory and Operations Course Sampler Robert A Phillips AnnapolisStar@gmail.com AESA Airborne Radar Theory and Operations Introduction Page 1
- 4. Copyright 2013 R.A. Phillips Objective Number 1 1) Learn how to interleave modes, intercept targets using advanced LPI techniques, and develop requirements for an AESA radar from the pilots point of view. AESA Radar engaging and launching missiles on three targets AESA Airborne Radar Theory and Operations Introduction: Page 2
- 5. Copyright 2013 R.A. Phillips Objective Number 2 2) Present the theory of an AESA Radar and learn how to design the air-air and air-ground modes from the requirements up Antenna Receive Pattern using a diamond layout with true symmetric Dolph Chebyschev sidelobe weighting (from supplied Radar Theory eBook) Clutter Template Pulse Compress FFT Square law Detector CFAR M of N Correlate Block Diagram for Search mode AESA Airborne Radar Theory and Operations Introduction: Page 3
- 6. Copyright 2013 R.A. Phillips Objective Number 3 3) Provide the simulations, tools, and references for “putting the theory into practice” 500 page interactive electronic book on class material including antennas, Space Time Adaptive Processing, Kalman filters, and automatic target recognition, with simulations & examples Win 7 Professional Radar mode design spread sheet with software AESA Radar Theory eBook "You cannot mandate productivity, you must provide the tools to let people become their best.“ Steve Jobs AESA Airborne Radar Theory and Operations Introduction: Page 4
- 7. Copyright 2013 R.A. Phillips Some of the Questions to be Answered 1) How do you design and compute the performance for the AESA search modes? 2) How do you design an AESA mode to track 50 targets ? 3) What is Space Time Adaptive Processing (STAP) and how do you design for it? 4) How can you use an AESA antenna to detect slow moving ground targets which are much smaller than the background clutter. 5) How do you design an automatic target detection and recognition mode? This sampler presents the top level charts from the course on how to answer these tough questions AESA Airborne Radar Theory and Operations Introduction: Page 5
- 8. Copyright 2013 R.A. Phillips Sampler 1) Design of an AESA Medium PRF Search Mode AESA Radar in Medium PRF search AESA Airborne Radar Theory and Operations Introduction: Page 6
- 9. Copyright 2013 R.A. Phillips MED PRF Search Block Diagram The Block Diagram for a MED PRF Search radar [Skolnick,fig 17.6] Altitude Speed Clutter Template [12] Sum Channel Compress FFT Square law detect CFAR We will use an AESA antenna and receiver with parameters like size, noise figure, power and cooling appropriate for a fighter type aircraft (from Stimson) to design the modes and compute the performance AESA Airborne Radar Theory and Operations Smallest allowable Target size m2 Skolnick Fig 17.12 Unfold Detects M of N Range Correlator M of N Doppler Correlator Target Reports Range, Doppler, Cross Section Introduction: Page 7
- 10. Copyright 2013 R.A. Phillips Clutter Template from Supplied Simulation Tail aspect Head • In this region the template tells us we should use a backend STC or guard channel • In this region we are competing with altitude line – Use special processing to blank returns • In this region the template tells us we are competing with noise only and we can use the noise PFA threshold. • In this region we are competing with Main Beam Clutter. Due to its magnitude we will use a notch filter The template [12] guides us in choosing a CFAR design AESA Airborne Radar Theory and Operations Introduction: Page 8
- 11. Copyright 2013 R.A. Phillips Baseline MED PRF Search Parameters Parameter Value Comments FFT Size 512 Controls S/N and scan rate PRF 70KHz For good tail aspect visibility CHIP 0.5mics For reduced clutter PCR 4 Higher average power M of N 3 of 7 Range correlation TFA 30sec Specification time between FA’s Freq Agile Look-Look Good LPI design Xmit Pulse 2mics Duty 14% Derived Avg Power parameters Pfa 471watts 5.8E-6 CFAR probability of false alarm The course will show the student how to select the parameters and enter them into the Mode Design spreadsheet AESA Airborne Radar Theory and Operations Introduction: Page 9
- 12. Copyright 2013 R.A. Phillips MED PRF Single Scan Performance Single Scan PD - Low PRF .VS. Medium PRF Cross Section 5m2 Chart from the mode design spreadsheet using VBA software from the eBook on Detection Theory (supplied with course) The Mode Design Spreadsheet 1) Guides the student in the designing a mode, 2) Captures the designs and 3) Compares the performance for different configurations AESA Airborne Radar Theory and Operations Introduction: Page 10
- 13. Copyright 2013 R.A. Phillips Sampler 2) How to Track 50 Targets with an AESA Radar AESA Radar engaging and launching missiles on six targets AESA Airborne Radar Theory and Operations Introduction Page 11
- 14. Copyright 2013 R.A. Phillips Vector Tracking Loop Error ∼∆kANT Steering vector k(α,β) For monopulse vector processing see [9] Haupt and eBook on antennas Compute Monopulse Error ∆kANT kT(θ,φ) target k(α,β) antenna steering General radar tracking loop Transform To NAV Coords Transform to ANT Coords Kalman Filter in NAV Target Relative Position Ownship position vector in NAV reference The Σ and ∆ channels are used to compute the error vector ∆k in ANT coordinates AESA Airborne Radar Theory and Operations Introduction: Page 12
- 15. Copyright 2013 R.A. Phillips Three Channel (Az,El,Range) Kalman [1] Gain Computation For n in 1..3 = Pn H Kn T ( HP H n T + Rn ) Extrapolate Where n is one of the 3 orthogonal channels Rng, Az , El XΦX = For n in 1..3 = ΦPn Q + Pn P n n −1 State Update For n in 1..3 Nav = X X + K n En P Update For n in 1..3 Pn= (1 − K n H ) Pn The lectures will define each matrix in the design AESA Airborne Radar Theory and Operations Introduction: Page 13
- 16. Copyright 2013 R.A. Phillips Typical Track Performance RMS Velocity Error Angle Error Tracking a Steady 3G S Turn at 20nm. RMS velocity errors typically approach 200+ft/sec and are entirely adequate to guide missiles to intercept AESA Airborne Radar Theory and Operations Introduction: Page 14
- 17. Copyright 2013 R.A. Phillips AESA Time Line 15 Target track interleaved with search while displaying a SAR image. Room for lots more!! AESA Airborne Radar Theory and Operations Introduction: Page 15
- 18. Copyright 2013 R.A. Phillips Sampler 3) Space Time Adaptive Cancellers AESA Airborne Radar Theory and Operations Introduction: Page 16
- 19. Copyright 2013 R.A. Phillips Space Time Adaptive Filters (Stimson, Haupt) • The STAP canceller can remove multiple sidelobe jammer(s) without prior knowledge of the jammer(s) location or antenna gains. • STAP uses an Interferometric (space based) canceller. • For each expected jammer we need one receiver and Auxilliary antenna with a gain larger than the sidelobes of the main antenna. Gain of AUX Target Adaptive Cancellers Stimson [3,Ch 40], Skolnick[4,Ch 9] AESA Airborne Radar Theory and Operations Standoff sidelobe jammer STAP computes jammer phase angles and antenna gains and applies a spaced based adaptive notch filter. By combining this with an FFT to separate moving targets we have a two dimensional Space – Time adaptive filter Introduction: Page 17
- 20. Copyright 2013 R.A. Phillips The Adaptive Canceller [7] Elbert V V V Main AUX2 AUXn Store samples from each channel in the rows of the H matrix H=[m a2 a3…an] X1 X2 ∑ See also Stimson [3,Pg509] The optimal weights X are the 1st column of the inverse of the covariance matrix (HTH)-1 Xn Note the order of the matrix inverse is equal to the number of channels i.e. two channels means we have to invert a 2x2 matrix Sum the weighted outputs of the multiple antennas to cancel the jammer. The space filter is a direct application of linear estimation theory [7] AESA Airborne Radar Theory and Operations Introduction: Page 18
- 21. Copyright 2013 R.A. Phillips Example of STAP With Multiple Jammers Example of STAP with 4 Jammers. 4 Aux horns Target 10deg 20deg 30deg See eBook on Antennas for detailed simulation of multiple jammers 40deg Weighted Sum The optimal weights are: x =1st Column of CovarianceMatrix −1 The cancelled jammer output equation is: Output=Main+x1 Aux1 +x 2 Aux2 +x 3 Aux3 +x 4 Aux4 One 5th order Matrix Inversion and 25 dot products of length 10 AESA Airborne Radar Theory and Operations Introduction: Page 19
- 22. Copyright 2013 R.A. Phillips FFT Before and After Cancellation The target cannot be seen in the FFT with 4 Sidelobe jammers. Notice the magnitude of the noise at 100 Q or more! Uncancelled Jammer + Target After cancellation the target is easily seen in the FFT and the noise is down to 5 quanta Cancelled Jammer + Target Example from eBook on Antennas AESA Airborne Radar Theory and Operations Introduction: Page 20
- 23. Copyright 2013 R.A. Phillips Sampler 4) Slow Ground moving target indicator Main Beam Clutter Canceller AESA Airborne Radar Theory and Operations Introduction: Page 21
- 24. Copyright 2013 R.A. Phillips Slow Moving Target Detection Combining the Interferometer technique (used in STAP) with multiple antenna beams we can implement a high performance mode to cancel main beam clutter and detect small slow moving targets in a situation which otherwise would be completely hopeless SAR display with outputs from the slow moving target detector One of the most impressive applications of an AESA canceller.. AESA Airborne Radar Theory and Operations Introduction: Page 22
- 25. Copyright 2013 R.A. Phillips Spatial vs Frequency Filtering Tail aspect Head Frequency Filtering: With an FFT we can separate targets with different Doppler frequencies. This fast moving target is separated by frequency from main beam clutter and is easily detected with an FFT FFT range/Doppler map Spatial Filtering This slow moving target, overwhelmed in an FFT by main beam clutter at the same frequency, can only be detected by spatial filtering with an interferometer The course will describe this essential diagram in detail AESA Airborne Radar Theory and Operations Introduction: Page 23
- 26. Copyright 2013 R.A. Phillips Slow Moving Targets and Clutter Stationary target at angle θt Large MBC Clutter at angle θc In a space diagram the target and clutter are separable θt θc Angle Space Map Slow moving target at angle θt Whereas in a normal FFT frequency diagram the target and clutter overlay each other and the smaller target cannot be detected Doppler Frequency Space Map A Spatial Notch with multiple antennas can remove the clutter AESA Airborne Radar Theory and Operations Introduction: Page 24
- 27. Copyright 2013 R.A. Phillips Slow Mover - Canceller [Stimson Pg321] k T (θ , φ ) k MBC (α , β ) The target at the same frequency as clutter -d/2 The phase for clutter at angle α ,β : d/2 Right Left Get α,β for each FFT Cell Get Gain for each FFT Cell MBC comes from a known angle α,β Rg x Filter matrix Rg x Filter matrix Cancel Clutter GLeft πd ϕc =R • k = sin(α ) cos( β ), G rel = GRight λ Using the canceller equation: G Output = Main - Aux M exp(− j 2ϕ ) GA The cancelled clutter for each filter is: Cancelled n = Leftn − Rightn exp(− j 2ϕc ) Recompute FFT CFAR Slow moving ground targets A little complicated but very powerful AESA Airborne Radar Theory and Operations Introduction: Page 25
- 28. Copyright 2013 R.A. Phillips S Sampler ATR Finds 3 S-300 Surface – Air Missile Launchers with Pd>0.95 in 2 sec S 5) Automatic Target Recognition Target Detection S Bushehr nuclear power plant from Google Maps AESA Airborne Radar Theory and Operations Introduction: Page 26
- 29. Copyright 2013 R.A. Phillips Automatic Target Detection Outline [13] SAR Targets + Clutter Data from MSTARS public website, algorithms from Lincoln labs and Mathcad image processing library CFAR Detector Get Enhanced Tgt Chips Binarize Image Detected targets sans clutter Clumped Detects Open/Close Shapes Edit Clutter False Tgts Compute Moments Statistics Library Clutter Shadow Removal Target Recognition Target List Detector uses general target signatures to find “military like” targets AESA Airborne Radar Theory and Operations Introduction: Page 27
- 30. Copyright 2013 R.A. Phillips Theory of Moments from [11] HU Characterization of an image by statistical moments like variance, and kurtosis, and invariant moments like the eigenvalues is a common approach in ATR. The Uniqueness theorem states that you can completely reconstruct an image with knowledge of the moments of the image. If you use amplitude, translation, scale and rotation invariant moments you increase the power of this approach E All three E’s in this example are uniquely identified by the same simple moments which are independent of where they are on the paper, their amplitude, scale or rotation We can also characterize tanks, trucks and guns by moments AESA Airborne Radar Theory and Operations Introduction: Page 28
- 31. Copyright 2013 R.A. Phillips Example Automatic Target Recognition[13] Enhanced M113 Chip from ATD with feature vector consisting of moments, stats and Pose=-30deg pose 1) Use the pose to index the library 2) Compute Score for each target in the library using feature vectors 3) The highest score is the ID Library Chips with same pose as detected target BTR60 M113 BMP2 Correlation 0.81 1 Eigenvalues 0.62 1 Area 0.89 1 Combined 0.45 1 Good Match Feature Vec BTR70 T72 M109 M2 HMMW M1 0.92 0.88 0.87 0.86 .91 .93 .85 0.71 0.61 0.73 0.54 .72 .70 .41 1 1 0.81 0.69 .91 .91 .67 0.66 0.54 0.51 0.32 .59 .59 .23 Comparison of feature vectors for each target in library AESA Airborne Radar Theory and Operations Introduction: Page 29
- 32. Copyright 2013 R.A. Phillips References 1) Decoupled Kalman filters for phased array radar tracking: Automatic Control, IEEE transactions on: Date of Publication: Mar 1983 Author(s):Daum F. Raytheon Company, Wayland, MA, USA 2) Blinchikoff and Zverev, “Filtering in the Time and Frequency Domain” 1975 3) Rabiner and Gold, Theory and Application of Digital Signal Processing 1975 4) Stimson, “Introduction to Airborne radar” 1998 5) Skolnick “Introduction to Radar” 1995 6) William Skillman “Radar Calculations” Artech House ,1983 7) “Estimation and Control of Systems” Elbert 1984 – Contains all aspects of linear estimation from least squares to the Kalman filter 9) Antenna Arrays - Randy Haupt IEEE Press 10) SDMS MSTARS Public Data Website https://www.sdms.afrl.af.mil/ Contains 1ft SAR images of military targets 11) M.-K. Hu, “Visual pattern recognition by moment invariants,” IRE Trans. Information Theory, vol. 8, no. 2, pp. 179–187, 1962. 12) Radar CFAR Thresholding in Clutter and MultipleTarget Situations Hermann Rohling AEG-Telefunken, IEEE Transactions On Aerospace and Electronic Systems VOL. AES-19, NO. 4 JULY 1983 Discusses clutter maps for describing clutter regions of differing clutter type. Excellent analysis of CA, GO CFAR and ordered statistic CFAR AESA Airborne Radar Theory and Operations Introduction: Page 30
- 33. Copyright 2013 R.A. Phillips References 13) MIT Lincoln Lab Journal Archives http://www.ll.mit.edu/publications/journal/journalarchives.html Vol 10, Number 2 - 1997 Vol 8, Number 1 - 1995 Vol 6, Number 1 - 1993 Provides overview of the Automatic Target Recognition and Detection including Super resolution SAR , CFAR’s and effects of polarization and resolution on recognition AESA Airborne Radar Theory and Operations Introduction: Page 31

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