In this paper, a target capacity based resource optimization (TC-RO) scheme is developed for multiple target tracking (MTT) application in radar networks. The key idea of this scheme is to coordinate the transmit power and dell time resource usage of multiple radars in order to increase the number of the targets that can be tracked with predetermined accuracy requirements. We adopt the Bayesian Cramér-Rao lower bound as a metric function to quantify the MTT accuracies, and build the TC-RO scheme as a non-smooth and non-convex optimization problem. To deal with this problem, we design an efficient three-step solution technique which incorporates relaxation and fine-tuning process. Specifically, we first relax the resulting optimization problem as a smooth one by applying sigmoid-type transformation to its objective, and then develop an appropriate method to find a local minimum to the relaxed non-convex problem with guaranteed convergence. After that, the local minimum of the relaxed problem is used as an initial point and a fine-tuning process is performed to search for a reasonable feasible solution to the original non-smooth optimization problem. Simulation results demonstrate that the proposed TC-RO scheme can greatly increase the target capacity of the radar network when compared with the traditional uniform allocation scheme.

中文翻译：

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雷达网络中基于目标容量的多目标跟踪资源优化


本文针对雷达网络中的多目标跟踪（MTT）应用开发了一种基于目标容量的资源优化（TC-RO）方案。该方案的关键思想是协调多个雷达的发射功率和戴尔时间资源的使用，以增加可以在预定精度要求下跟踪的目标的数量。我们采用贝叶斯 Cramér-Rao 下界作为度量函数来量化 MTT 精度，并将 TC-RO 方案构建为非光滑非凸优化问题。为了解决这个问题，我们设计了一种有效的三步解决技术，其中结合了松弛和微调过程。具体来说，我们首先通过将 sigmoid 型变换应用于其目标，将生成的优化问题松弛为平滑问题，然后开发一种适当的方法来找到松弛非凸问题的局部最小值，并保证收敛。之后，将松弛问题的局部最小值作为初始点，并进行微调过程，以搜索原始非光滑优化问题的合理可行解。仿真结果表明，与传统的统一分配方案相比，所提出的TC-RO方案可以极大地提高雷达网络的目标容量。