Ding Lintao, Shi Chenguang, Qiu Wei, Zhou Jianjiang
Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Science and Technology on Electro-Optic Control Laboratory, Luoyang 471009, China.
Sensors (Basel). 2020 Feb 26;20(5):1269. doi: 10.3390/s20051269.
Radar network systems have been demonstrated to offer numerous advantages for target tracking. In this paper, a low probability of intercept (LPI)-based joint dwell time and bandwidth optimization strategy is proposed for multi-target tracking in a radar network. Since the Bayesian Cramer-Rao lower bound (BCRLB) provides a lower bound on parameter estimation, it can be utilized as the accuracy metric for target tracking. In this strategy, in order to improve the LPI performance of the radar network, the total dwell time consumption of the underlying system is minimized, while guaranteeing a predetermined tracking accuracy. There are two adaptable parameters in the optimization problem: one for dwell time, and the other for bandwidth allocation. Since the nonlinear programming-based genetic algorithm (NPGA) can solve the nonlinear problem well, we develop a method based upon NPGA to solve the resulting problem. The simulation results demonstrate that the proposed strategy has superiority over traditional algorithms, and can achieve a better LPI performance of this radar network.
雷达网络系统已被证明在目标跟踪方面具有诸多优势。本文针对雷达网络中的多目标跟踪问题，提出了一种基于低截获概率（LPI）的联合驻留时间和带宽优化策略。由于贝叶斯克拉美罗下界（BCRLB）为参数估计提供了下限，因此可将其用作目标跟踪的精度度量。在该策略中，为提高雷达网络的LPI性能，在保证预定跟踪精度的同时，将底层系统的总驻留时间消耗降至最低。优化问题中有两个自适应参数：一个用于驻留时间，另一个用于带宽分配。由于基于非线性规划的遗传算法（NPGA）能够很好地解决非线性问题，我们开发了一种基于NPGA的方法来解决由此产生的问题。仿真结果表明，所提出的策略优于传统算法，并且能够实现该雷达网络更好的LPI性能。
