Non-Line-of-Sight Mitigation via Lagrange Programming Neural Networks in TOA-Based Localization

被引：2

作者：

Han, Zi-Fa ^{[1
]}

Leung, Chi-Sing ^{[1
]}

So, Hing Cheung ^{[1
]}

Sum, John ^{[2
]}

Constantinides, A. G. ^{[3
]}

机构：

[1] City Univ Hong Kong, Dept Elect Engn, Kowloon Tong, Hong Kong, Peoples R China

[2] Natl Chung Hsing Univ, Taichung 40227, Taiwan

[3] Univ London Imperial Coll Sci Technol & Med, London, England

来源：

NEURAL INFORMATION PROCESSING, PT III | 2015年 / 9491卷

关键词：

TOA localization; Non-line-of-sight (NLOS); LPNN; TIME;

D O I：

10.1007/978-3-319-26555-1_22

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A common measurement model for locating a mobile source is time-of-arrival (TOA). However, when non-line-of-sight (NLOS) bias error exists, the error can seriously degrade the estimation accuracy. This paper formulates the problem of estimating a mobile source position under the NLOS situation as a nonlinear constrained optimization problem. Afterwards, we apply the concept of Lagrange programming neural networks (LPNNs) to solve the problem. In order to improve the stability at the equilibrium point, we add an augmented term into the LPNN objective function. Simulation results show that the proposed method provides much robust estimation performance.

引用

页码：190 / 197

页数：8

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