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Receive-Beam Resource Allocation for Multiple Target Tracking With Distributed MIMO Radars

机译:分布式MIMO雷达的多目标跟踪接收波束资源分配

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摘要

A distributed multiple-input multiple-output (MIMO) radar system is capable of tracking multiple targets under the “defocused transmit-focused receive” (DTFR) operating mode, in which each transmitter forms a completely defocused beam to illuminate the whole surveillance region and each receiver adopts a focused beam to attain a higher resolution. However, when operating in this mode, there exists a resource optimization problem on the allocation of receive-beams. To address this problem, a receive-beam resource allocation (RBRA) strategy is proposed in this paper. The key mechanism is to implement the optimal allocation between receive-beams and targets based on the feedback information in the tracking recursion cycle, with the objective of improving the worst tracking accuracy with multiple targets. Since the posterior Cramér-Rao lower bound (PCRLB) provides a lower bound on the accuracy of target state estimates, it is derived and adopted as an optimization criterion. It is shown that the optimal RBRA is a multidimensional nonconvex assignment problem that is NP-hard. We propose an efficient convex relaxation optimization to solve it. Numerical results demonstrate the superior performance of the proposed strategy in terms of the worst case tracking root mean-square errors.
机译:分布式多输入多输出(MIMO)雷达系统能够在“散焦的发射-聚焦接收”(DTFR)操作模式下跟踪多个目标,其中每个发射器形成一个完全散焦的光束,以照亮整个监视区域并每个接收器都采用聚焦光束以获得更高的分辨率。然而,当以这种模式操作时,在接收波束的分配上存在资源优化问题。为了解决这个问题,本文提出了一种接收波束资源分配(RBRA)策略。关键机制是基于跟踪递归循环中的反馈信息,实现接收光束和目标之间的最佳分配,目的是提高多个目标的最差跟踪精度。由于后Cramér-Rao下界(PCRLB)为目标状态估计的准确性提供了下界,因此将其推导并用作优化标准。结果表明,最优RBRA是一个NP难的多维非凸分配问题。我们提出了一种有效的凸松弛优化来解决它。数值结果表明,该算法在最坏情况下跟踪均方根误差方面具有优越的性能。

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