Bayesian Sequential Parameter Estimation by Cognitive Radar With Multiantenna Arrays

In this paper we consider the problem of Bayesian sequential parameter estimation of extended targets for cognitive radar with multi-antenna arrays using adaptive waveforms. The target is modeled as a complex Gaussian random process. Using iterative waveform transmission, the cognitive radar estimates the target's characteristic parameters and updates its probabilistic model based on new measurements. The adaptive waveform is designed by minimizing the conditional entropy from the posterior density of the model parameters. We analyze the performance of the developed Bayesian sequential estimation algorithm and derive expressions for the signal-to-noise ratio gain, the asymptotical posterior Cramer Rao bound, and the mutual information gain. The analysis and numerical simulations demonstrate that the adaptive sequential Bayesian estimator yields accelerated convergence of the estimate towards its true value and a smaller estimation error compared with the conventional Bayesian estimator that uses fixed waveform transmission under Gaussian or non-Gaussian noise.