| 摘要: |
| 最小二乘估计算法常用于基于测距的源定位，然而，当移动基站与基站间呈非视距(Non Line of Sight, NLOS)路径时，最小二乘估计算法无法提供理想的定位精度。为了克服此问题，研究人员提出多类算法识别并消除NLOS误差。然而，现存的算法存在高运行时间的开销问题。为此，提出基于特征矢量的NLOS误差检测的定位 (Eigenvector-Based NLOS Error Identification Localization, E-NIL) 算法。E-NIL算法先利用基于测距数据的统计特性识别NLOS误差，然后，将NLOS误差看成确定加性噪声项，再利用误差函数与它的特征矢量间的互相关，寻找NLOS误差值。最后，再删除这些NLOS项，并依据这些无NLOS误差的数据估计移动基站的位置。实验数据表明，提出的E-NIL算法在定位精度和复杂度方面优于同类算法。 |
| 关键词: 源定位 双向到达时间 非视距误差 最小二乘估计 特征矢量 |
| DOI：DOI:10.3969/j.issn.1672-2337.2019.02.009 |
| 分类号:TN911.7；TP393 |
| 基金项目:国家自然科学基金青年科学基金（No.61701331）；四川省教育厅自然科学基金一般项目（No.18ZB0498）；四川省水利厅2017年科研计划项目（No.SL2017-01） |
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| Eigenvector-Based NLOS Error Identification Localization Algorithm |
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LIU Xiaoxia, LI Fang
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1.Department of Information Engineering, Sichuan Water Conservancy Vocational Technology College, Chongzhou 611231, China;2.College of Computer Science, Chongqing University, Chongqing 400044, China
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| Abstract: |
| Least squares estimation algorithms are widely used in range-based source localization. These methods cannot provide desirable accuracy in the case of a non line of sight (NLOS) path between mobile station and base stations. Various algorithms have been proposed to identify and mitigate this error. However, they have a large run-time overhead. Therefore, an eigenvector-based NLOS error identification localization (E-NIL) algorithm is proposed in this paper. The E-NIL algorithm identifies NLOS error based on statistical features of range measurements, and the NLOS error is considered as deterministic additive term. Then, the E-NIL algorithm finds the NLOS error value using the autocorrelation function of the error and its eigenvector. Simulation results demonstrate superiority of the proposed method in comparison with the state-of-the-art algorithms in terms of accuracy and complexity. |
| Key words: source localization time of arrival NLOS error least squares estimation eigenvector |
