Compared with conventional phased array radar, the collocated multiple-input multiple-out (C-MIMO) radar is envisioned to offer greater freedom in waveform design and can simultaneously manage different beams to track multiple targets in the simultaneous multi-beam (SM) working mode. In this study, aiming at the multiple targets tracking (MTT) problem, a joint beam selection integrated with power and bandwidth allocation (JSPBA) scheme for the C-MIMO radar is proposed in the SM mode. By incorporating the modified particle filter, the predicted conditional Cramér–Rao lower bound (PC-CRLB) is calculated, which gives a measure of the best achievable performance for targets tracking. Then, the optimisation model is established with the aim of improving the worst case of PC-CRLB to achieve better performance of the worst-case tracking. Next, a three-step solution method is proposed to solve the JSPBA problem by converting the non-convex problem into a series of convex problems. At last, after the solutions are fed back to the control centre to guide the beam generation in the next tracking epoch, a cognitive tracking system is established. Simulation results confirm the superiority of the proposed method in improving the MTT performance in C-MIMO radar system.

中文翻译：

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并置天线的MIMO雷达中认知多目标跟踪的联合波束选择和资源分配

与传统的相控阵雷达相比，并置多输入多出（C-MIMO）雷达可提供更大的波形设计自由度，并能在同步多波束（SM）工作中同时管理不同的波束以跟踪多个目标模式。在这项研究中，针对多目标跟踪（MTT）问题，在SM模式下，提出了一种结合了功率和带宽分配（JSPBA）方案的C-MIMO雷达联合波束选择方案。通过合并改进的粒子滤波器，可以计算预测的条件Cramér-Rao下限（PC-CRLB），从而为目标跟踪提供了最佳可实现性能的度量。然后，建立优化模型，其目的在于改善PC-CRLB的最坏情况，以获得更好的最坏情况跟踪性能。下一个，提出了一种三步求解方法，通过将非凸问题转化为一系列凸问题来解决JSPBA问题。最后，在将解决方案反馈到控制中心以在下一跟踪纪元中引导束生成之后，建立认知跟踪系统。仿真结果证实了该方法在提高C-MIMO雷达系统MTT性能方面的优越性。