ATI's Fundamentals of Radar Target Tracking course

Summary: The objective of this 3-day course is to introduce engineers, scientists, managers, and military personnel to the fundamental problems involved with Radar Target Tracking. The course first reviews the fundamentals of System Dynamics, and the nature of unknown and/or random noise. This course reviews the topics necessary to integrate Radar Sensor characteristics with Target Dynamics resulting in a Tracking System. The Radar Sensor fundamental characteristics include its beam to capture and measure target coordinates, with measurement random noise, and the data rate. State Space approach with linear quadratic criteria to control and observe system states will also be included. These techniques will also be shown to be fundamental in Stochastic Systems dynamics, as well as Kalman Filtering. Specific Target Tracking performance objectives will be considered which include tracking noise reduction, as well as target escape from the radar beam. Included in this course is the Optimal Kalman Tracking and the stationary/steady state tracking solution resulting in an a - b Target Tracker with a considerable savings of computer operations, capacity and time calculation. Methods and techniques will be shown how to use the a - b Target Tracker to obtain performances, which approach the Kalman Tracker. Examples, including MATLAB numerical results, will be included to demonstrate the topics/techniques used in Radar Target Tracking. Instructor: Paul Kalata is an Associate Professor of Electrical and Computer Engineering at Drexel University, Philadelphia. Previously, he was a Principle Engineer at RCA Missile and Surface Radar (now Lockheed Martin), Moorestown, NJ where he developed fundamental target tracking principles for the SPY-1 Radar for the AEGIS Weapon System. Consulting experience included Navy, Air Force and private industry in the area of Target Tracking and Stochastic Control issues. At Drexel, his research interest is in the area of Stochastic Systems: Kalman Filtering and Control. He has authored over 75 published articles and reports specifically in Kalman Filtering and Target Tracking. What You Will Learn: How Radar and Target characteristics, constraints and objectives define the tracker Review of State Space Dynamics both Linear & Non-Linear How system characteristics: stability, controllability, observability affect the tracker How to understand uncertainty/noise models; measures: mean/median, variance, co-variance What estimation is: bias, error variance; performance; mean-square-error estimation What the Kalman Filter is: basic problem, stationary/steady state filter; extended Kalman Filter How to apply the Kalman Filter to the radar/target target problem, stationary/steady state tracking How to obtain ??? target tracking: Tracking Index formula, gains and performance How to handle anomalies and counter-measures: missing measurements; range gate pull-off What the worst case tracking is: probability of target escape, H-8 target tracking How to relate other????? tracking issues: continuous tracker, prediction, smoothing, track fusion Course Outline: Introduction: the radar/target tracking system, tracking constraints & performance objective. System Components: radar: rotary, phased array, and rotary/phased array; target: aircraft, missile, projectile Coordinate Systems: radar: range, azimuth, and elevation; target: velocity vector, thrust, turn; intercept control (ship/aircraft): cartesian; conversion one to another. State Space Dynamical Systems: linear, continuous state dynamics; discrete-time state dynamics both in matrix/vector formulation with solutions; non-linear state dynamics; linearization, examples. Output/Measurement: linear, continuous time matrix/vector; discrete time; both in matrix/vector formulation; non-linear measurements: linearization System Characteristics: stability: continuous/discrete systems; controllability: controllers, quadratic methods; observability: observers; definitions: conditions, tests; examples Uncertainty and Noise: randomness and probability; continuous/discrete, uniform, Gaussian; measures: mean/median, variance, co-variance Linear, Discrete Time Stochastic System: matrix/vector formulation, state & measurement noise: mean & co-variance dynamics State Estimation: estimation forms: non-linear/linear estimates; estimation error: bias, error variance; performance: unbiased, minimum variance; examples Optimal Estimation: maximum likelihood, maximum-a-posteriori, mean-square-error, conditional mean, examples State Space Estimation: linear system, Gaussian noise, error conditional mean, recursive form = > Kalman Filter Kalman Filter: basic/simplest problem, state/measurement noise correlation, one form Kalman Filter, stationary/steady state filter Extended Kalman Filter: non-linear dynamics/measurements Radar/Target Application: Kalman target tracker: prediction/correction tracker, prediction/prediction tracker, steady state tracker a - b Target Tracker: coordinate decoupling, position/velocity/acceleration, stationary/steady state tracking, track initiation Tracking Index: normalized a - b (a - b - g) analytical tracking formulas: gains, performance, track initiation Anomalies and Counter-Measures: track rate variations, missing/bad measurements; range gate pull-off Worst Case Tracking: probability of target escape, H-8 target tracking Related a - b Tracking Issues: continuous tracker, prediction tracker, track smoothing, track fusion Tuition: Tuition for this three-day course is $1490 per person at one of our scheduled public courses. Onsite pricing is available. Please call us at 410-956-8805 or send an email to ati@ATIcourses.com. Register Now Without Obligation