A contribution to performance prediction for probabilistic data association tracking filters
D.J. Kershaw, Robin J. Evans · IEEE Transactions on Aerospace and Electronic Systems · 1996
The probabilistic data association (PDA) algorithm for tracking in clutter contains a stochastic (data-dependent) Riccati equation for updating the estimation error covariance matrix. This note details a simple analytic approximation to the stochastic Riccati equation that allows precomputation of the estimation error covariance matrices. The potential of the approximation for performance analysis of PDA-based tracking algorithm is demonstrated using a simple example.