基于图论的主被动混合雷达天线选择和功率分配

      Graph theory-based antenna selection and power allocation for active-passive hybrid radar

      • 摘要: 主被动混合雷达系统充分利用环境中的被动信号,融合主动雷达和被动雷达的优势,可在日益复杂的电磁环境中实现高探测效能。本文针对主被动混合雷达系统的目标检测问题,研究主动雷达的天线选择与离散功率分配以及被动天线的选择。为了在主动雷达总功率和主被动天线数目的约束下最大化目标检测性能,基于图论构建了以检测器输出信噪比为性能指标的联合优化问题。该问题本质上是非线性整数规划问题,计算的复杂度会随着主被动发射天线数量的增加呈指数增长。本文将被动天线选择视为二部图中顶点集,并将主动雷达天线选择和发射功率的分配联合起来转化为两个顶点集的匹配问题,进而将联合离散优化问题转换为稳定匹配问题,并提出基于GS(Gale-Shapley)的联合优化算法。仿真结果表明,本文所提算法在显著降低复杂度的同时可获得接近最优的目标检测性能。

         

        Abstract: Active-passive hybrid radar systems make full use of passive signals in the environment and integrate the advantages of active radar and passive radar, enabling efficient radar detection in the increasingly complex electromagnetic environment. Focusing on the target detection problem of active-passive hybrid radar systems, this paper investigates the antenna selection and discrete power allocation for active radar as well as the antenna selection for passive radar. To maximize the target detection performance subject to the constraints on the total transmit power of the active radar, the number of active antennas and passive antennas to be processed. a joint optimization problem with the detector output signal-to-noise ratio (SNR) as the performance metric is formulated based on graph theory. The original problem is a nonlinear integer programming problem, whose computational complexity increases drastically with the growth of the number of active and passive transmit antennas. In this paper, the selection of passive antennas is regarded as a vertex set in a bipartite graph, and the joint optimization of antenna selection and transmit power allocation for active radar is transformed into a matching problem between two vertex sets, converting the joint discrete optimization problem into a stable matching problem. Accordingly, a joint optimization algorithm based on the Gale-Shapley (GS) algorithm is proposed. Simulation results demonstrate that the proposed algorithm significantly reduces the computational complexity while achieving near-optimal target detection performance.

         

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