Introduction to Target Detection, Tracking and Sensor Data Fusion Huimin Chen University of New Orleans Department of Elec...
Information Extraction and Fusion <ul><li>Extract  maximum possible  amount of information from each sensor by using appro...
Outline <ul><li>Target Motion Models </li></ul><ul><li>Sensor Measurement Models </li></ul><ul><li>Major Techniques and Ch...
Target Tracking Overview <ul><li>Tracking  consists of </li></ul><ul><li>●  estimation of the current state of a target (e...
Target Motion Models <ul><li>Stationary or deterministic motion:  If you detect a target, you know where it is </li></ul><...
Sensor Measurement Models <ul><li>Target detection probability:  Depends on sensor-target geometry, signal-to-noise ratio ...
Measurement Origin Uncertainty <ul><li>Finding a signal from multiple-frame noisy measurements </li></ul>1-D example: dete...
Joint Target Detection and Parameter Estimation Technique <ul><li>Form the likelihood function with weighted average of al...
Finding Unknown Number of Targets Missile defense application:  to detect and estimate missile trajectories (2 targets here)
Joint Target Detection and Parameter Estimation Technique <ul><li>Single target in clutter:  GLRT can detect target at 4dB...
Tracking Maneuvering Targets in Clutter  <ul><li>Data association and filtering (state estimation) are related:  </li></ul...
Tracking Maneuvering Targets in Clutter (Cont’d) The more models the better? Figure used with courtesy of Prof. Yaakov Bar...
Tracking Maneuvering Targets in Clutter (Cont’d) <ul><li>Hybrid systems  characterized by </li></ul><ul><ul><li>State:  ki...
Tracking Maneuvering Targets in Clutter (Cont’d) <ul><li>Trackers with best trade-off between tracking accuracy and comput...
Tracking Closely-Spaced Objects Sensor 1 Located at [0, 0] Sensor 2 Located at [180km, 80km] Tracking of Spawning Targets:...
Tracking Closely-Spaced Objects <ul><li>Unresolved measurements:  a single (merged) measurement from multiple targets at t...
Tracking with Multiple Sensors: Data Fusion Techniques <ul><li>Prerequisites for successful data fusion </li></ul><ul><ul>...
Summary <ul><li>Tracking performance is usually measured by the  mean-square estimation error  (matrix) of target state  ...
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Intro to Multitarget Tracking for CURVE

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Intro to Multitarget Tracking for CURVE

  1. 1. Introduction to Target Detection, Tracking and Sensor Data Fusion Huimin Chen University of New Orleans Department of Electrical Engineering New Orleans LA 70148 Tutorial Slides Made Based on YBS&LI’s Tracking Book
  2. 2. Information Extraction and Fusion <ul><li>Extract maximum possible amount of information from each sensor by using appropriate target and sensor models </li></ul><ul><li>Quantify the corresponding uncertainties in target detection and estimation </li></ul><ul><li>Fuse the information from various sources accounting for their uncertainties </li></ul>
  3. 3. Outline <ul><li>Target Motion Models </li></ul><ul><li>Sensor Measurement Models </li></ul><ul><li>Major Techniques and Challenges in Target Tracking </li></ul><ul><ul><li>Target motion uncertainty  Multiple models </li></ul></ul><ul><ul><li>Measurement origin uncertainty  Data association techniques </li></ul></ul><ul><ul><li>Sensor resolution limit  Data fusion from multiple platforms </li></ul></ul><ul><li>Relevance to CURVE project </li></ul>
  4. 4. Target Tracking Overview <ul><li>Tracking consists of </li></ul><ul><li>● estimation of the current state of a target (e.g., position, velocity) based on uncertain measurements (e.g., range, bearing) </li></ul><ul><li>● calculation of the accuracy associated with the state estimate (e.g., mean square error matrix) </li></ul>
  5. 5. Target Motion Models <ul><li>Stationary or deterministic motion: If you detect a target, you know where it is </li></ul><ul><li>Nearly constant velocity or other linear type motion: Kalman filter can be applied to track the target </li></ul><ul><li>Maneuvering target: Target can change its motion at an unknown time instance </li></ul><ul><ul><li>Acceleration along tangential direction </li></ul></ul><ul><ul><li>Acceleration along normal direction </li></ul></ul><ul><li>Many maneuver models are available (Li&Jilkov Survey) but the filter needs to choose the appropriate one adaptively . </li></ul>
  6. 6. Sensor Measurement Models <ul><li>Target detection probability: Depends on sensor-target geometry, signal-to-noise ratio (SNR), etc. </li></ul><ul><li>Sensor measurement: </li></ul><ul><ul><li>Range </li></ul></ul><ul><ul><li>Bearing </li></ul></ul><ul><ul><li>Amplitude </li></ul></ul><ul><li>Target originated measurements: radar returns from a target </li></ul><ul><li>False alarms: radar returns from clutter </li></ul><ul><li>Measurement origin uncertainty: The filter does not know which measurement comes from which target (or clutter) </li></ul>
  7. 7. Measurement Origin Uncertainty <ul><li>Finding a signal from multiple-frame noisy measurements </li></ul>1-D example: detect a signal and estimate its location
  8. 8. Joint Target Detection and Parameter Estimation Technique <ul><li>Form the likelihood function with weighted average of all measurements and find the maximum likelihood estimate − maximum likelihood with probabilistic data association (ML-PDA) </li></ul><ul><li>Whether we accept the ML-PDA estimate or not − use generalized likelihood ratio test (GLRT) for signal present vs. no signal </li></ul><ul><li>Estimation accuracy − quantified by a scalar information reduction factor scaled to the Cramer-Rao lower bound of the measurement-origin-certain case </li></ul><ul><li>Very effective for single track-before-detect under low signal to noise ratio (SNR) </li></ul>
  9. 9. Finding Unknown Number of Targets Missile defense application: to detect and estimate missile trajectories (2 targets here)
  10. 10. Joint Target Detection and Parameter Estimation Technique <ul><li>Single target in clutter: GLRT can detect target at 4dB SNR (in average, at the end of the radar signal processor) with over 95% power </li></ul><ul><li>Multiple targets in clutter: Minimum description length (MDL) can choose the correct number of targets at 4.4dB average SNR (assuming similar SNRs and large separations among the targets) with over 95% power </li></ul><ul><li>Estimation accuracy: The mean-square estimation error can be obtained and used in the objective function for path planning </li></ul>
  11. 11. Tracking Maneuvering Targets in Clutter <ul><li>Data association and filtering (state estimation) are related: </li></ul><ul><ul><li>Data association: hard decision (e.g., assignment) vs. soft decision (e.g., PDA) </li></ul></ul><ul><ul><li>Model switching: hard decision (based on some logic) vs. soft decision (probabilistic) </li></ul></ul><ul><li>The development of filtering techniques </li></ul><ul><ul><li>60s: Kalman filter, α - β filter (steady state Kalman filter with fixed gain) </li></ul></ul><ul><ul><li>70s: extended Kalman filter (linearization) </li></ul></ul><ul><ul><li>80s: multiple model techniques, the most successful one is interacting multiple model (IMM) </li></ul></ul><ul><ul><li>90s: variable structure IMM, nonlinear filters (e.g., particle filter, unscented Kalman filter) </li></ul></ul>
  12. 12. Tracking Maneuvering Targets in Clutter (Cont’d) The more models the better? Figure used with courtesy of Prof. Yaakov Bar-Shalom
  13. 13. Tracking Maneuvering Targets in Clutter (Cont’d) <ul><li>Hybrid systems characterized by </li></ul><ul><ul><li>State: kinematic and possibly feature components, evolve according to a model driven by continuous noise </li></ul></ul><ul><ul><li>Models: model state governed by a Markov chain, one model can switch to another </li></ul></ul><ul><li>Want to have an appropriate set of models </li></ul><ul><ul><li>Efficient recursive scheme for hybrid systems </li></ul></ul><ul><ul><li>Simultaneously run several Kalman filter modules </li></ul></ul><ul><ul><li>Output </li></ul></ul><ul><ul><ul><li>Model probabilities </li></ul></ul></ul><ul><ul><ul><li>Combined state estimate weighted by the model probabilities </li></ul></ul></ul><ul><ul><ul><li>Corresponding error covariance </li></ul></ul></ul><ul><ul><li>Can work with data association algorithms (e.g., JPDA, MHT via multiframe assignment) </li></ul></ul>
  14. 14. Tracking Maneuvering Targets in Clutter (Cont’d) <ul><li>Trackers with best trade-off between tracking accuracy and computational load: IMM in conjunction with multiframe assignment/MHT </li></ul><ul><li>IMM with 2-D assignment can track over 800 targets on a Pentium 400 processor in real time </li></ul>
  15. 15. Tracking Closely-Spaced Objects Sensor 1 Located at [0, 0] Sensor 2 Located at [180km, 80km] Tracking of Spawning Targets: Use geometrically dispersed sensors to obtain better resolution
  16. 16. Tracking Closely-Spaced Objects <ul><li>Unresolved measurements: a single (merged) measurement from multiple targets at the same sensor resolution cell, with large measurement error </li></ul><ul><li>Data association: multiframe assignment with new constraints indicating the many-to-one assignment situation </li></ul><ul><li>Approach: use successive Lagrangian relaxation to solve the constrained optimization problem, suboptimal but close to optimal </li></ul><ul><li>Need top-M best solutions to account for the measurement origin uncertainty </li></ul><ul><li>Tracker: Kalman/IMM + Deep PDA with time depth </li></ul>
  17. 17. Tracking with Multiple Sensors: Data Fusion Techniques <ul><li>Prerequisites for successful data fusion </li></ul><ul><ul><li>Sensor registration (alignment) </li></ul></ul><ul><ul><li>Reliable statistical description of the uncertainties in each sensor’s data </li></ul></ul><ul><ul><li>Reliable assessment of estimation accuracy </li></ul></ul><ul><li>Centralized configuration: all data associations and tracking are carried out at a central location − best performance, but requires high communication bandwidth </li></ul><ul><li>Distributed configuration: each sensor has its own data processor, fusion can be done “on demand” − often suboptimal , but robust with less bandwidth requirement </li></ul>
  18. 18. Summary <ul><li>Tracking performance is usually measured by the mean-square estimation error (matrix) of target state  A tracker should provide such information </li></ul><ul><li>Track-before-detect is very effective to find low SNR targets using multiple-frame measurements with good knowledge of target motion </li></ul><ul><li>Tracking maneuvering targets under clutter environment is challenging due to both target motion and measurement origin uncertainties </li></ul><ul><li>UNO team has developed effective data association and filtering algorithms to account for such uncertainties </li></ul><ul><li>Fusing measurements (or target state estimates) from multiple geometrically dispersed sensors can improve tracking accuracy </li></ul><ul><li>Tracking performance should be considered in developing the objective function for path planning/re-planning </li></ul>

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