A Robust TOA Source Localization Algorithm Based on LPNN

被引：1

作者：

Wang, Hao ^{[1
]}

Feng, Ruibin ^{[1
]}

Leung, Chi-Sing ^{[1
]}

机构：

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

来源：

NEURAL INFORMATION PROCESSING, ICONIP 2016, PT I | 2016年 / 9947卷

关键词：

Source location; Time-of-arrival; Outliers; LPNN; PROGRAMMING NEURAL-NETWORKS; OPTIMIZATION; LOCATION;

D O I：

10.1007/978-3-319-46687-3_41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One of the traditional models for finding the location of a mobile source is the time-of-arrival (TOA). It usually assumes that the measurement noise follow a Gaussian distribution. However, in practical, outliers are difficult to be avoided. This paper proposes an l(1)-norm based objective function for alleviating the influence of outliers. Afterwards, we utilize the Lagrange programming neural network (LPNN) framework for the position estimation. As the framework requires that its objective function and constraints should be twice differentiable, we introduce an approximation for the l(1)-norm term in our LPNN formulation. From the simulation result, our proposed algorithm has very good robustness.

引用

页码：367 / 375

页数：9

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