An Efficient Power Allocation Strategy for Maneuvering Target Tracking in Cognitive MIMO Radar

In this paper, an efficient power allocation (PA) strategy is developed for maneuvering target tracking (MTT) in the collocated MIMO radar. The mechanism of our strategy is to implement the optimal PA based on the prior target maneuvering information in the tracking cycle. The predicted conditional Cramer–Rao lower bound (PC-CRLB) is derived, normalized and adopted as the optimization criterion, since the PC-CRLB is based on the most recently realized measurement and provides a more accurate lower bound than the standard posterior CRLB (PCRLB). We fully demonstrate that the established optimization model is convex. By exploiting the monotonic decreasing property of the objective function, an efficient sequential relaxation-based solver is proposed for the solution, where the PA for one target is identified that whether should be fixed on the minimum level at each iteration. Simulation results show better tracking performance compared with the uniform allocation, and improved efficiency, compared with convex optimization tools.