Abstract: The adaptive optimization of detection thresholds for tracking in clutter is an important issue in tracking. The probabilistic data association filter (PDAF) algorithm for tracking in clutter contains a stochastic (data-dependent) Riccati equation for updating the estimation error covariance matrix. The adaptive optimization of detection thresholds for tracking in Rayleigh clutter environment is obtained by combining the prior detection threshold optimization criterion and the analytic approximation to the (deterministic) modified Riccati equation. From the simulation results and the research, it is found that (1) the performance of the presented method is superior to that of the fixed false alarm probability method; (2) only when the signal to noise ratio is greater than a certain value (in Rayleigh clutter environment, the value is 1.57 for 2 dimensional measurement and four-sigma validation gate), the adaptive optimization of detection thresholds for tracking in Rayleigh fading clutter environment can be obtained.

Key words: detection, t hr eshold, tr acking, optimization, PDAF