Bayesian Multiple Target Tracking
Get the solutions to your most challenging tracking problems with this up-to-date resource. Using the Bayesian inference framework, the book helps you design and develop mathematically sound algorithms for dealing with tracking problems involving multiple targets, multiple sensors, and multiple platforms. The book shows you how non-linear Multiple Hypothesis Tracking and the Theory of Unified Tracking are successful methods when multiple target tracking must be performed without contacts or association.
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Bayesian Inference and Likelihood Functions
Single Target Tracking
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approach array association likelihood function assume assumptions Bayes Bayesian filtering Bayesian inference cell Chapter compute covariance data association hypothesis Data Fusion defined detection and tracking discrete distribution on target ellipse estimate expected value false alarms Figure Gaussian distribution given information update integrated likelihood ratio joint state space Kalman filtering Likelihood Principle likelihood ratio density likelihood ratio detection linear log-likelihood ratio density Markov matrix mean measurement error measurement likelihood ratio measurement log-likelihood ratio measurement space motion model motion update multiple target tracking Nodestar noise nonlinear number of targets obtain peak performance posterior distribution prior distribution probability density probability distribution produce propagation radar random variable result sample scan association hypothesis scan association likelihood Section sensor data sensor responses shows signal level signal-to-noise ratio single target tracking situation submarine target motion target present target state space target strength target's position Theorem threshold tracker transition unified tracking vector X(tk