In this paper, an effective solution is proposed for joint beam and power scheduling (JBPS) in the netted Colocated MIMO (C-MIMO) radar systems for distributed multi-target tracking (MTT). At its core, the proposed solution includes a distributed fusion architecture that reduces the communication requirements while maintaining the overall robustness of the system. The distributed fusion architecture employs the covariance intersection (CI) fusion to address the unknown information correlations among radar nodes. Each C-MIMO radar node in the network can generate a time-varying number of beams with controllable transmitting power by waveform synthesis, thus is capable of accomplishing multiple tracking tasks simultaneously. To maximize the global MTT performance of the radar network, the proposed JBPS solution implements an online resource scheduling, regarding both the generated beams and the transmitted power of all radar nodes, based on the feedback of the MTT results. A scaled accuracy-based objective function is designed to quantify the global MTT performance while properly taking into account different target priorities on resource allocation. The Bayesian Cramér-Rao lower bound (BCRLB) for CI fusion rule is derived and utilized as the constituent of the objective function since it provides a lower bound on the accuracy of the target state estimates. As the formulated JBPS problem is non-convex, we propose a fast reward-based iterative descending approach to solve it effectively. Numerical results show that the proposed JBPS can deliver superior performance in terms of maximizing the overall MTT performance while possessing high flexibility on the resource allocation regarding different target priorities.

中文翻译：

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网状协同定位 MIMO 雷达系统中分布式多目标跟踪的资源调度

在本文中，针对分布式多目标跟踪 (MTT) 的网状 Colocated MIMO (C-MIMO) 雷达系统中的联合波束和功率调度 (JBPS) 提出了一种有效的解决方案。在其核心，所提议的解决方案包括分布式融合架构，该架构在保持系统整体稳健性的同时降低了通信要求。分布式融合架构采用协方差交叉（CI）融合来解决雷达节点之间未知的信息相关性。网络中的每个C-MIMO雷达节点可以通过波形合成产生随时间变化的发射功率可控的波束，从而能够同时完成多个跟踪任务。为了最大限度地提高雷达网络的全局 MTT 性能，所提出的 JBPS 解决方案基于 MTT 结果的反馈，针对所有雷达节点的生成波束和发射功率实施在线资源调度。基于缩放精度的目标函数旨在量化全局 MTT 性能，同时适当考虑资源分配的不同目标优先级。派生了 CI 融合规则的贝叶斯 Cramér-Rao 下限 (BCRLB) 并将其用作目标函数的组成部分，因为它提供了目标状态估计精度的下限。由于公式化的 JBPS 问题是非凸的，我们提出了一种快速的基于奖励的迭代下降方法来有效地解决它。