Distributed consensus filtering for discrete-time nonlinear systems with non-Gaussian noise

• Published in: Signal processing Vol. 92; no. 10; pp. 2464 - 2470
• Main Authors: Li, Wenling, Jia, Yingmin
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2012; Elsevier
• Subjects: Applied sciences; Average-consensus; Detection, estimation, filtering, equalization, prediction; Distributed estimation; Dynamical systems; Exact sciences and technology; Filtering; Gaussian; Gaussian mixture; Information, signal and communications theory; Mathematical models; Noise; Non-Gaussian; Sensors; Signal and communications theory; Signal, noise; Statistics; Telecommunications and information theory; Unscented Kalman filter; Gaussian mixture; Distributed estimation; Average-consensus; Unscented Kalman filter; Performance evaluation; Target tracking; Filtering; Logarithmic function; Discrete time systems; Non gaussian noise; Measurement sensor; Mixture theory; 1/f noise; Non linear system; Distributed computing; Distributed processing; Finite mixture method; Gaussian process; Simulation; Discrete time; Signal processing; Sensor array; Signal detection
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2012.03.009

This paper studies the problem of distributed estimation for a class of discrete-time nonlinear non-Gaussian systems in a not fully connected sensor network environment. The non-Gaussian process noise and measurement noise are approximated by finite Gaussian mixture models. A distributed Gaussian mixture unscented Kalman filter (UKF) is developed in which each sensor node independently calculates local statistics by using its own measurement and an average-consensus filter is utilized to diffuse local statistics to its neighbors. A main difficulty encountered is the distributed computation of the Gaussian mixture weights, which is overcome by introducing the natural logarithm transformation. The effectiveness of the proposed distributed filter is verified via a simulation example involving tracking a target in the presence of glint noise. ► A distributed UKF for nonlinear systems with Gaussian mixture noise is proposed. ► The Gaussian mixture weight is computed by introducing the natural logarithm transformation. ► The proposed filter is robust to sensor failure and scalable. ► Results show the effectiveness of the proposed filter compared to the centralized version.