Bayesian Cramer-Rao Bound for multiple targets tracking in MIMO radar
Phuoc Vu, Alexander M. Haimovich, Braham Himed · 2017
Theoretical performance bounds are developed for tracking multiple targets in multiple-input multiple-output (MIMO) radar systems with widely distributed antennas. In previous work, we proposed a direct tracker, which eliminates the need for explicit association of observations to tracks, thus improving tracking performance compared to conventional trackers that estimate time delays and Doppler. In this paper, we develop the Bayesian Cramer Rao Bound (BCRB) for the performance of direct tracking of multiple targets in a distributed MIMO radar. A first-order approximation linearizes the observations with respect to the targets state vector. The BCRB is developed for Swerling Type 1 targets. The theoretical performance bounds are applied to demonstrate the performance of direct trackers versus conventional trackers.