多重目标直接定位的子空间分解压缩感知算法

Multi-target Direct Position Determination using Subspace based Compressive Sensing

本文针对传统无线定位系统先收集参数信息,再用于目标定位,因中间参数无法保证与目标实际位置相匹配而引起的定位误差问题,提出了一种新型的目标直接定位(Direct Position Determination,DPD)算法。该算法考虑到直接定位系统中阵列信号模型的运用以及目标在空间上的稀疏特性,将阵列信号处理中经典MUSIC算法与压缩感知理论(Compressive Sensing,CS)相结合,对阵列接收信号进行特征空间分解,以信号子空间为初始残差代入贪婪运算,有效减少了噪声影响,在不以目标数量为先验的条件下,也能以布置很少的无线基站实现对目标位置的精确恢复,同时,通过对该算法在迭代过程中的逐级优化,降低了系统成本和复杂度。实验仿真结果显示,将本文算法应用于直接定位系统模型,其在抗噪性能,误差率以及复杂度上都明显优于传统算法。

In this paper,a novel direct position determination（ DPD） algorithm was proposed to complement the conventional two-step localization approach,in which the measurement should be associated with the correct target. Considering the usage of array signal model and the inherent sparsity of targets in spatial domain,we combined the MUSIC method and the traditional CS algorithm to localize targets in the region of interest. We first calculated the signal subspace by decomposing the empirical covariance matrix of the received signal,and then took it as the original residual into the greedy iteration,which can greatly decrease the affection of the noise. Moreover,it has been proved that this proposed algorithm can accurately recover a sparse signal with a high probability without knowing the number of targets as a priori,and reduce the cost and complexity of the algorithm by deploying only a small number of base stations. Finally,we conduct a comprehensive set of simulations whose results demonstrate the superiority of our method over the existing algorithms.