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.

中文翻译：

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认知MIMO雷达中机动目标跟踪的有效功率分配策略

本文提出了一种有效的功率分配（PA）策略，用于并置MIMO雷达中的机动目标跟踪（MTT）。我们策略的机制是根据跟踪周期中的先前目标操纵信息来实现最佳功率放大器。由于PC-CRLB基于最近实现的测量，并且比标准后CRRB（下限）更准确，因此可以推导出预测的条件式Cramer-Rao下界（PC-CRLB），将其标准化并用作优化标准。 PCRLB）。我们充分证明所建立的优化模型是凸的。通过利用目标函数的单调递减性质，为解决方案提出了一种有效的基于序列松弛的求解器，确定一个目标的功率放大器，是否应在每次迭代中将其固定在最小水平上。仿真结果表明，与均匀分配相比，跟踪性能更好，与凸优化工具相比，效率更高。