东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2012, Vol. 33 ›› Issue (7): 917-921.DOI: -

• 论著 • 上一篇    下一篇

基于假设检验的NLOS确定及最小残差定位算法

程龙;吴成东;张云洲;贾子熙;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60874103);;

Hypothesis testing based NLOS identification and minimum residual localization algorithm

Cheng, Long (1); Wu, Cheng-Dong (1); Zhang, Yun-Zhou (1); Jia, Zi-Xi (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Cheng, L.
  • About author:-
  • Supported by:
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摘要： 针对室内非视距传播会降低定位精度的情况,提出基于假设检验的NLOS确定算法,并在此基础上提出基于粒子群优化算法的最小残差定位算法.通过联合信号接收强度模型和到达时间模型,利用假设检验的方法确定当前信号是否受到非视距污染.仿真结果表明,与标准差分析法相比,所提出的方法具有计算量小、不需要太多的传播模型参数等特点,具有较高的正确率.在确定信号传播环境后,在非视距干扰比较严重的情况下,提出了基于粒子群优化算法的最小残差定位算法,定位精度优于加权最小二乘法和Fang氏算法.

关键词: 非视距, 定位算法, 假设检验, 最小残差, 粒子群优化算法

Abstract: The localization accuracy could be severely degraded due to the non-line-of-sight (NLOS) propagation in the indoor environment. A NLOS signal detection algorithm based on hypothesis testing was proposed, and a new particle swarm optimization algorithm based minimum residual localization algorithm was presented for the wireless sensor networks. The received signal strength and time-of-arrival (TOA) were used to distinguish between LOS and NLOS propagation based on hypothesis testing. Simulation results showed that compared with deviation analysis and fewer parameters of propagation model, this method has lower computation complexity. Finally, particle swarm optimization algorithm based minimum residual localization algorithm was proposed after propagation detection algorithm. The proposed method outperforms the existing weighted least square method and Fang method with better estimation accuracy.

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