基于离散鸡群压缩感知的WSNs多目标定位

对无线传感器网络（WSNs）多目标定位问题进行研究，提出一种基于离散鸡群压缩感知的多目标定位方法。首先给出离散鸡群算法（DCSO）相关定义，设计离散鸡群编码方式和迭代进化策略，在此基础上，构建基于压缩感知（CS）的WSNs多目标定位模型，对测量矩阵和稀疏矩阵进行合理选取，并将离散鸡群算法应用于CS信号重构算法中，实现对稀疏度未知多目标位置信息的精确重构。仿真结果表明，与OMP和MLE定位算法相比，该方法具有较高的多目标定位精度。

Multiple Target Localizationin WSNs via CS Reconstruction Method Based on Discrete CSO Algorithm

The multiple target localization problem of WSNs is studied, and a multiple target localization method based on discrete chicken swarm optimization (DCSO) and compressed sensing (CS) theory is proposed. Firstly, some definitions related to DCSO algorithm are given, and the discrete chicken coding method and the iterative evolution strategy are designed. Based on this, a WSNs application model based on CS is established, and the measurement matrix and sparse matrix are chosen. Finally, the DCSO is applied to the CS sparse signal reconstruction algorithm, which realizes unknown sparsity multi-objective information reconstruction. The simulation results show that, compared with OMP and MLE, this method has better effect in multiple target locating precision.