基于压缩感知的室内多目标无线定位算法

针对现存无线传感器网络定位算法中需要采集、存储和处理大量数据导致运算量较大与能耗过高的问题,提出了一种改进的基于贝叶斯压缩感知的多目标定位算法.该算法利用锚节点对监控区域的划分,结合贝叶斯压缩感知理论将多目标定位问题转换为稀疏信号重构的问题.针对传统观测矩阵难以实现的缺陷,该算法中改进观测矩阵的设计可实现且与稀疏变换基相关性较低,进而使得算法的重构性能较高,从而降低了定位的误差.仿真结果表明,与现有的一些方法相比,所提算法在保证较低的计算复杂度的情况下更加充分地利用了网络节点,有效提高了定位精度,同时具有较强的鲁棒性.

An indoor multi-target wireless location algorithm based on compressed sensing

For the problem that a large amount of data is needed to be collected,stored and processed in the existing localization algorithms for wireless sensor networks and the computation is large and energy consumption is high,an improved multi stage localization algorithm based on Bayesian compressive sensing is proposed.In this algorithm,the problem of multi target location is transformed into the problem of sparse signal reconstruction by using anchor nodes to divide the monitoring area and Bayesian compressive sensing theory.For the shortcoming of traditional observation matrix,the improved observation matrix design can be realized and has low correlation with sparse transform basis,so the reconstruction performance of the algorithm is high and the positioning error is reduced.Simulation results show that,compared with some existing methods,the proposed algorithm makes full use of the network nodes,improves the location accuracy effectively and has strong robustness.