Lagrange programming neural networks for time-of-arrival-based source localization

被引：27

作者：

Leung, Chi Sing ^{[1
]}

Sum, John ^{[2
]}

So, Hing Cheung ^{[1
]}

Constantinides, Anthony G. ^{[3
]}

Chan, Frankie K. W. ^{[1
]}

机构：

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

[2] Natl Chung Hsing Univ, Inst Technol Management, Taichung 40227, Taiwan

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

来源：

NEURAL COMPUTING & APPLICATIONS | 2014年 / 24卷 / 01期

关键词：

Source localization; Stability; Neural dynamics;

D O I：

10.1007/s00521-013-1466-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Finding the location of a mobile source from a number of separated sensors is an important problem in global positioning systems and wireless sensor networks. This problem can be achieved by making use of the time-of-arrival (TOA) measurements. However, solving this problem is not a trivial task because the TOA measurements have nonlinear relationships with the source location. This paper adopts an analog neural network technique, namely Lagrange programming neural network, to locate a mobile source. We also investigate the stability of the proposed neural model. Simulation results demonstrate that the mean-square error performance of our devised location estimator approaches the Cram,r-Rao lower bound in the presence of uncorrelated Gaussian measurement noise.

引用

页码：109 / 116

页数：8

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