A Kalman Framework Based Mobile Node Localization in Rough Environment Using Wireless Sensor Network

• Published in: International journal of distributed sensor networks Vol. 2015; no. 5; p. 841462
• Main Authors: Chu, Hao, Wu, Cheng-dong
• Format: Journal Article
• Language: English
• Published: London, England Hindawi Publishing Corporation 01.01.2015; SAGE Publications; Sage Publications Ltd. (UK); Sage Publications Ltd; Hindawi - SAGE Publishing
• Subjects: Accuracy; Algorithms; Engineering research; Error analysis; Errors; Global positioning systems; GPS; Hypotheses; Hypothesis testing; Kalman filtering; Localization; Location-based systems; Maximum likelihood method; Networks; Position (location); Propagation; Remote sensors; Sensors; Standard deviation; Technology application; Wireless networks; Wireless sensor networks
• ISSN: 1550-1329; 1550-1477; 1550-1477
• DOI: 10.1155/2015/841462

Since the wireless sensor network (WSN) has the performance of sensing, processing, and communicating, it has been widely used in various environments. The node localization is a key technology for WSN. The accuracy localization results can be achieved in ideal environment. However, the measurement may be contaminated by NLOS errors in rough environment. The NLOS errors could result in big localization error. To overcome this problem, we present a mobile node localization algorithm using TDOA and RSS measurements. The proposed method is based on Kalman framework and utilizes the general likelihood ratio method to identify the propagation condition. Then the modified variational Bayesian approximation adaptive Kalman filtering is used to mitigate the NLOS error. It could estimate the mean and variance of measurement error. The simulation results demonstrate that the proposed method outperforms the other methods such as Kalman filter and H ∞ filter.