非视距环境中的无线网络残差加权定位算法

Residual Weighted Based Wireless Localization Algorithm in NLOS Environments

无线定位已成为物联网应用的重要研究内容,为减小非视距(NLOS)误差对传统定位算法的精度的影响。因此提出一种基于目标临时位置估计的残差幂次方加权定位算法。算法核心在于先利用分组定位得到不同目标节点估计位置,并将位置之间的差值定义为临时定位残差。然后区别于传统残差加权定位算法,本文提出用残差的高次幂作为加权函数,并通过仿真搜索最优的加权函数。仿真结果表明,当LOS-SN数目不小于2时,本文算法在定位精度上远高于传统的NLOS抑制定位算法。和传统的残差加权定位算法(RWGH)相比,本文所提算法的定位精度提高了近60%,同时降低了对LOS-SN个数的要求。和半正定规划(SDP)相比,定位精度提高了近38%,并降低了计算复杂度。

Wireless localization has become an important issue in internet of things. It is widely known that the non-line-of-sight(NLOS)error will greatly reduce the accuracy of traditional localization algorithm. Therefore,a residual power-square weighted localization algorithm based on target temporary location estimation is proposed to solve this problem. First,the static nodes(SNs)are divided into several subgroups to obtain variable position estimations of target node(TN),and then define the deviation between these positions as the temporary positioning residual. Different from the traditional residual weighted localization algorithm,the proposed algorithm uses the high power of the residual as the weight function and finds the optimal weight function through simulations. Simulation results show that the accuracy of the proposed algorithm outperforms the traditional algorithms when the number of LOS-SN is no less than two. The proposed algorithm is more superior in positioning accuracy to the traditional NLOS suppression localization algorithm. Compared with the traditional residual weighted localization algorithm(RWGH),the proposed algorithm improves the positioning accuracy about 60%,while the requirement of LOS-SN number is reduced . Moreover,in comparing with the semi-definite programming(SDP)method,our positioning accuracy increases nearly38%,and the corresponding complexity is reduced.