一种非视距环境下的目标定位算法

PDF下载HTML

Title:

A target localization algorithm in NLOS environments

作者:

齐小刚^1,2, 张海洋¹, 魏倩¹

1. 西安电子科技大学 数学与统计学院，陕西 西安 710071;
2. 西安电子科技大学 宁波信息技术研究院，浙江 宁波 315200

Author(s):

QI Xiaogang^1,2, ZHANG Haiyang¹, WEI Qian¹

1. School of Mathematics and Statistics, Xi’dian University, Xi’an 710071, China;
2. Ningbo Information Technology Institute, Xi’dian University, Ningbo 315200, China

关键词:

目标定位; 非视距; 到达时间; 平衡参数; 二分法; 广义信赖域子问题; 凸优化; 误差抑制

Keywords:

target localization; non-line-of-sight (NLOS); time-of-arrival (TOA); balance parameters; bisection; generalized trust region sub-problem (GTRS); convex optimization; error mitigate capability

分类号:

TP393

DOI:

10.11992/tis.201912012

摘要:

针对机器人、无人机和其他智能系统的位置信息，研究了非视距(non line of sight, NLOS)环境中基于到达时间(time of arrival,TOA)测距的目标定位问题。在建模过程中，通过引入平衡参数来抑制NLOS误差对定位精度的影响，并成功将定位问题的形式与一个广义信赖域子问题(generalized trust region subproblem,GTRS)框架进行耦合。与其他凸优化算法不同的是，本文没有联合估计目标节点的位置和平衡参数，而是采用了一种迭代求精的思想，算法可以用二分法高速有效地进行求解。 所提算法与已有的算法相比，不需要任何关于NLOS路径的信息。此外，与大多数现有算法不同，所提算法的计算复杂度低，能够满足实时定位的需求。仿真结果表明：该算法具有稳定的NLOS误差抑制能力，在定位性能和算法复杂度之间有着很好的权衡。

Abstract:

The location information of robots, UAVs, and other intelligent systems is crucial. This paper mainly studies the target location problem based on TOA ranging in the non-line-of-sight (NLOS) environment. In the process of modeling, the influence of NLOS error on positioning precision is restrained, and the form of the localization problem is coupled with a generalized trust-region subproblem (GTRS) framework. Instead of joint estimation of the location and balance parameter of the object nodes, an iterative refinement idea is adopted, and the algorithm can be solved quickly and effectively by dichotomy. In contrast to existing algorithms, the proposed algorithm does not need information about the NLOS path. In addition, unlike most existing algorithms, the proposed algorithm has a low computational complexity and can meet the need of real-time localization. The simulation results show that the proposed algorithm has stable NLOS error mitigation capability and a good balance between localization performance and algorithm complexity.