Summary:
The objective of this 3day 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.
Tuition:
Instructor:
What You Will Learn:
 How Radar and Target characteristics, constraints and objectives define the tracker
 Review of State Space Dynamics both Linear & NonLinear
 How system characteristics: stability, controllability, observability affect the tracker
 How to understand uncertainty/noise models; measures: mean/median, variance, covariance
 What estimation is: bias, error variance; performance; meansquareerror 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 countermeasures: missing measurements; range gate pulloff
 What the worst case tracking is: probability of target escape, H8 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; discretetime state dynamics both in matrix/vector formulation with solutions; nonlinear state dynamics; linearization, examples.
 Output/Measurement: linear, continuous time matrix/vector; discrete time; both in matrix/vector formulation; nonlinear 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, covariance
 Linear, Discrete Time Stochastic System: matrix/vector formulation, state & measurement noise: mean & covariance dynamics
 State Estimation: estimation forms: nonlinear/linear estimates; estimation error: bias, error variance; performance: unbiased, minimum variance; examples
 Optimal Estimation: maximum likelihood, maximumaposteriori, meansquareerror, 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: nonlinear 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 CounterMeasures: track rate variations, missing/bad measurements; range gate pulloff
 Worst Case Tracking: probability of target escape, H8 target tracking
 Related a  b Tracking Issues: continuous tracker, prediction tracker, track smoothing, track fusion
Tuition:
Tuition for this threeday course is $1490 per person at one of our scheduled public courses. Onsite pricing is available. Please call us at 4109568805 or send an email to ati@aticourses.com.

