结合最陡下降算法的室内NLOS场景下TDOA/AOA融合算法  

TDOA/AOA Fusion Algorithm in Indoor NLOS Scenario Combined with Steepest Descent Algorithm

作　　者：王成晓 沈重[1,2] 张鲲[1,2,3] Wang Chengxiao;Shen Chong;Zhang Kun(School of Information and Communication Engineering,Hainan University,Haikou 570228,China;State Key Laboratory of Marine Resources Utilization,Hainan University,Haikou 570228,China;Hainan Tropical Ocean College MTA Education Center,Sanya 572022,China)

机构地区：[1]海南大学信息与通信工程学院,海南海口570228 [2]海南大学南海海洋资源利用国家重点实验,海南海口570228 [3]海南热带海洋学院MTA教育中心,海南三亚572022

出　　处：《海南大学学报（自然科学版）》2019年第4期299-305,共7页Natural Science Journal of Hainan University

基　　金：国家自然科学基金(61461017);海南省高等学校科学研究重点项目(Hnky2019ZD-35);海南省自然科学基金创新研究团队项目(2017CXTD0004)

摘　　要：在利用传统Chan算法进行目标节点位置估算的基础上,提出了一种结合最陡下降算法SDA(Steepest Descent Algorithm)的TDOA(Time Difference of Arrival)/AOA(Angle of Arrival)融合算法,通过迭代消除由NLOS(Non Line of Sight)误差引起的误差因子,达到有效提高定位精度的目的.实验结果表明:本文提出的结合SDA的TDOA/AOA融合算法在复杂的室内环境下可以有效提高定位精度和定位稳定性,相对于传统的基于Chan算法的TDOA/AOA定位算法,定位精度提高28%.In the report, based on the traditional location estimation using Chan algorithm, a TDOA(Time Difference of Arrival)/AOA(Angle of Arrival) fusion algorithm combined with the steepest descent algorithm(SDA) was proposed, the iteratively were used to eliminate the error factor caused by NLOS(Non Line of Sight) error to achieve the purpose of effectively improving positioning accuracy. The results showed that the TDOA/AOA fusion algorithm combined with the steepest descent algorithm can effectively improve the positioning accuracy and positioning stability in complex indoor environments. Compared with the traditional TDOA/AOA positioning algorithm based on Chan algorithm, the positioning accuracy is improved by 28%.

关 键 词：UWB定位技术 TDOA/AOA融合算法 非视距误差 CHAN算法 最陡下降算法 

分 类 号：TN919.72[电子电信—通信与信息系统]