Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking

Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection thresho...
Ausführliche Beschreibung

Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results. Ausführliche Beschreibung