Cognitive MIMO Radar Beamforming for Target Tracking Using a BCRB-based Criterion

Abstract

This paper proposes a cognitive beamforming method for target tracking using multiple-input multiple-output (MIMO) radar. This method minimizes a Bayesian performance criterion on direction-of-arrival (DOA) estimation error with respect to the transmit signal auto-correlation matrix. Traditionally, the Bayesian Cramér-Rao bound (BCRB) serves as an optimization criterion for cognitive radars. However, when the corresponding deterministic Fisher information is parameter-dependent, the BCRB is unachievable, even asymptotically. In order to obtain a reliable criterion in the asymptotic region, the semi-expected Cramér-Rao bound (SECRB) is adopted. Our approach utilizes the SECRB for target tracking as a criterion in order to sequentially determine the transmit signal auto-correlation matrix based on past measurements. Simulations indicate that the proposed method outperforms DOA estimation using the BCRB-based cognitive approach and MIMO radar with orthogonal signals. This paper demonstrates that the proposed method automatically focuses the transmit beampattern towards the target direction within fewer steps compared to the BCRB-based cognitive approach.