A novel robust multivariate Laplace distribution-based distributed consensus information fusion

• Published in: Measurement : journal of the International Measurement Confederation Vol. 225; p. 113986
• Main Authors: Jiang, Zihao, Zhou, Weidong, Guo, Yu, Zhang, Chao, Tian, Lu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.02.2024
• Subjects: Heavy-tailed measurement noise; Hybrid consensus; Multivariate Laplace distribution; Robust information filter; Sensors network; Variational Bayesian; Hybrid consensus; Variational Bayesian; Multivariate Laplace distribution; Robust information filter; Heavy-tailed measurement noise; Sensors network
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2023.113986

The multivariate Laplace distribution (MLD) possesses a lesser set of prior parameters compared to the Student’s t-distribution, thus simplifying the modeling of measurement data subject to heavy-tailed noise corruption. This short communication introduces a novel distributed hybrid consensus fusion algorithm utilizing the MLD modeling and variational Bayesian approach. Firstly, we formulate the measurement noise associated with individual sensor nodes and introduce the hierarchical Gaussian form of MLD to enhance the feasibility of variational inference. Secondly, we opt for the inverse-Wishart (IW) distribution to serve as the conjugate prior to the positive definite matrix, enabling adaptive parameter learning within the MLD context. As a result, a novel hierarchical Gaussian state–space model (SSM) is established for multi-sensors network. Subsequently, we derive a novel local information filter to acquire higher precision information and innovation vector–matrix pairs, outperforming the Student’s t distribution-based filter. These derivations are grounded in the new hierarchical SSM, and the vector–matrix pairs aid in information fusion across the network. Lastly, we present a distributed robust consensus filter for fusing local information using a hybrid consensus approach that devoid of a central fusion node. Simulation results demonstrate that the proposed consensus filter can obtain better estimation accuracy under the interference of heavy-tailed measurement outliers. •We assumes the measurement noise of each sensor follows a multivariate Laplace distribution.•We have proposed a localized information filter using the variational Bayesian methodology.•We have suggested a hybrid consensus filter designed to ensure robust distributed fusion.•Simulation results illustrate that the algorithm we have proposed improves estimation accuracy.