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Weighted optimization-based distributed Kalman filter for nonlinear target tracking in collaborative sensor networks

journal contribution
posted on 08.12.2017 by Jie Chen, Jiahong Li, Shuang-Hua Yang, Fang Deng
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the “best” estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61321002, Grant 61304254, and Grant 61120106010, in part by the Program for Changjiang Scholars and Innovative Research Team in University under Grant IRT1208, and in part by the Beijing Outstanding Ph.D. Program Mentor under Grant 20131000704

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Cybernetics

Volume

47

Issue

11

Pages

3892 - 3905

Citation

CHEN, J. ... et al, 2016. Weighted optimization-based distributed Kalman filter for nonlinear target tracking in collaborative sensor networks. IEEE Transactions on Cybernetics, 47 (11), pp. 3892-3905.

Publisher

© IEEE

Version

SMUR (Submitted Manuscript Under Review)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016

Notes

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISSN

2168-2267

eISSN

2168-2275

Language

en

Exports