基于压缩感知的两阶段多目标定位算法

Two-phase Multi-target Localization Algorithm Based on Compressed Sensing

针对传感器网络中基于接收信号强度(Received Signal Strength,RSS)的多目标定位具有天然稀疏性的问题,提出了基于压缩感知的两阶段多目标定位算法,该算法将基于网格的多目标定位问题分解为粗定位和细定位两个阶段。粗定位阶段,根据序贯压缩感知原理确定最优观测次数,然后利用l_p最优化问题重构出目标所在的初始候选网格;细定位阶段,由四分法不断划分候选网格,根据最小残差原则估计目标在候选网格中的确切位置。仿真结果表明,相较于传统的基于l_1最优化的多目标定位算法,基于压缩感知的两阶段多目标定位算法在目标个数未知的场景下具有更优的定位性能,且明显减少了定位时间。

The RSS-based multi-target location has the natural property of the sparsity in wireless sensor networks.In this paper,a two-phase multi-target localization algorithm based on compressed sensing was proposed.This algorithm divides the grid-based target localization problem into two phases:coarse location phase and fine location phase.In the coarse location phase,the optimal number of measurements is determined according to the sequential compressed sen-sing,and then the locations of the initial candidate grids are reconstructed by l p optimization.In the fine location phase,all candidate grids are continually divided by quadripartition method,and the accurate locations of targets in the corresponding candidate grids are estimated by using the minimum residual principle.Compared with the traditional multi-target localization algorithm using l 1 optimization,the simulation results show that the proposed localization algorithm has better localization performance when the number of targets is unknown.Meanwhile,the localization time is significantly reduced.