Prior Knowledge-Based Simultaneous Multibeam Power Allocation Algorithm for Cognitive Multiple Targets Tracking in Clutter

Abstract

In this paper, a power allocation scheme for tracking multiple targets, with radar measurements either target generated or false alarms, is developed for colocated multiple-input multiple-output (MIMO) radar system. Such a system adopts a multibeam concept, in which multiple simultaneous transmit beams are synthesized by different probing signals from various colocated transmitters. To ensure that the limited power resource can be exploited effectively, we adjust the transmit power of each beam according to the prior knowledge predicted from the tracking recursion cycle. Specifically, the whole algorithm can be viewed as a reaction of the cognitive transmitters to the environment, in order to improve the worst case tracking performance of the multiple targets. By incorporating an information reduction factor (IRF), the Bayesian Cramér-Rao lower bound (BCRLB) gives a measure of the best achievable performance for target tracking in clutter. Hence, it is derived and utilized as an optimization criterion for the simultaneous multibeam power allocation algorithm. The optimization problem is nonconvex and is solved by the modified gradient projection (MGP) method in this paper. Simulation results show that the proposed algorithm significantly outperforms equal power allocation, in terms of the worst case tracking root mean-square error (RMSE).