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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 2510903, 8 pages
Research Article

Neighborhood Kalman Estimation for Distributed Localization in Wireless Sensor Networks

1Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China
2Department of Precision Instrument, Tsinghua University, Beijing 100084, China

Received 10 November 2015; Revised 7 January 2016; Accepted 26 January 2016

Academic Editor: Paolo Addesso

Copyright © 2016 Xiaochu Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Accurate location information plays an important role in the performance of wireless sensor networks since many mission applications depend on it. This paper proposes a fully distributed localization algorithm based on the concept of data fusion, allowing the full intranodes information including the correlations among estimates to take part in the algorithm. By properly constructing and updating the estimates as well as the corresponding covariance matrices, the algorithm can fuse intranodes information to generate more accurate estimates on the sensor locations with a fast speed. Finally, numerical simulations are given as examples to demonstrate the effectiveness of the algorithm.