Abstract Networked radar systems have the ability to enhance the tracking performance by working in a cognitive manner. In the cognitive radar network, resource allocation technique can be employed to improve the system performance. In this paper, a joint threshold optimization and power allocation (JTOPA) strategy for target tracking by radar network in clutter is proposed containing two main stages. At the detection stage, we introduce the Bayesian tracker-aware detector to adaptively adjust the threshold based on the predicted target state information. At the transmitting stage, the limited power resource is optimally assigned to each radar node. By incorporating the improved information reduction factor (IIRF), the Bayesian Cramer-Rao lower bound (BCRLB) is utilized as the optimization criterion for the JTOPA strategy. The JTOPA is a two-variable nonconvex optimization problem. We develop a spectral projected gradient based method to solve the problem. Simulations show that the proposed JTOPA strategy effectively improves the tracking performance compared with the uniform threshold setting and power allocation in the cognitive radar network.

中文翻译：

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nn 杂波中目标跟踪的认知雷达网络联合阈值优化与功率分配

摘要 网络化雷达系统具有通过以认知方式工作来提高跟踪性能的能力。在认知雷达网络中，可以采用资源分配技术来提高系统性能。在本文中，提出了一种用于雷达网络在杂波中进行目标跟踪的联合阈值优化和功率分配（JTOPA）策略，该策略包含两个主要阶段。在检测阶段，我们引入了 Bayesian tracker-aware 检测器，根据预测的目标状态信息自适应地调整阈值。在发射阶段，有限的功率资源被最优地分配给每个雷达节点。通过结合改进的信息缩减因子 (IIRF)，贝叶斯 Cramer-Rao 下界 (BCRLB) 被用作 JTOPA 策略的优化标准。JTOPA 是一个二变量非凸优化问题。我们开发了一种基于光谱投影梯度的方法来解决这个问题。仿真表明，与认知雷达网络中的统一阈值设置和功率分配相比，所提出的JTOPA策略有效地提高了跟踪性能。