An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis

Kang Li; Jinghua Li; Yutao Jiao; Guoru Ding; Shihua Dong

doi:10.1117/12.2589432

20 January 2021 An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis

Kang Li, Jinghua Li, Yutao Jiao, Guoru Ding, Shihua Dong

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 1171912 (2021) https://doi.org/10.1117/12.2589432
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

RSS-based target localization algorithms are usually derived from channel path-loss model where the measurement noise is generally assumed to obey Gaussian distribution. In this paper, we approximate the realistic measurement noise distribution by a Gaussian mixture model and proposed an improved mixture noise analysis-based RSS target localization algorithm employing expectation maximization, called Gaussian mixture-expectation maximization (GMEM) approach, to estimate target coordinates iteratively, which can be efficiently used for tackling unknown parameters of maximum likelihood estimation and non-convex optimization. Simulations show a considerable performance gain of our proposed localization algorithm in 2-D wireless sensor network.

Citation Download Citation

Kang Li, Jinghua Li, Yutao Jiao, Guoru Ding, and Shihua Dong "An expectation maximization solution for RSS target localization by Gaussian mixture noise analysis", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 1171912 (20 January 2021); https://doi.org/10.1117/12.2589432

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