Hu Tianchen, Liao Kefei, Ouyang Shan, Wang Haitao
Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2022 Aug 25;22(17):6400. doi: 10.3390/s22176400.
As a new radar system with improved performance, distributed multiple-input multiple-output (MIMO) radar provides a new idea for the development of netted radar. Aiming at the limited resource allocation problem of netted radar, this paper extends the idea of distributed MIMO radar to netted radar and proposes a resource scheduling algorithm for multitarget imaging in distributed netted radar based on the maximum scheduling benefits. Under the condition of the cognition of the target characteristics, the algorithm comprehensively considers the angle and dwell time to complete the multiradar and multitarget matching. Then it uses the principle of compressed sensing to calculate the pulse resources required for sparse imaging of each target on the corresponding radar. In this paper, the scheduling benefit of a radar system is expressed by weighting the three factors of the scheduling success rate, the hit value rate and the pulse resource consumption rate. The resource scheduling model is established according to the maximum scheduling benefits and solved using a heuristic algorithm. The simulation results show that compared with the traditional algorithm, this method improves the scheduling benefits of the radar system.
作为一种性能得到提升的新型雷达系统，分布式多输入多输出（MIMO）雷达为组网雷达的发展提供了新思路。针对组网雷达资源分配受限问题，本文将分布式MIMO雷达的思想扩展到组网雷达，提出了一种基于最大调度效益的分布式组网雷达多目标成像资源调度算法。在目标特性已知的条件下，该算法综合考虑角度和驻留时间来完成多雷达与多目标匹配。然后利用压缩感知原理计算各目标在相应雷达上进行稀疏成像所需的脉冲资源。本文通过对调度成功率、命中价值率和脉冲资源消耗率这三个因素进行加权来表示雷达系统的调度效益。根据最大调度效益建立资源调度模型，并采用启发式算法求解。仿真结果表明，与传统算法相比，该方法提高了雷达系统的调度效益。
