Multi-sensor Suboptimal Fusion Student's $t$ Filter

Abstract

A multi-sensor fusion Student's $t$ filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. Driven from an information-theoretic optimization, the approach extends the single sensor Student's $t$ Kalman filter based on the suboptimal arithmetic average (AA) fusion approach. To ensure computationally efficient, closed-form $t$ density recursion, reasonable approximation has been used in both local-sensor filtering and inter-sensor fusion calculation. The overall framework accommodates any Gaussian-oriented fusion approach such as the covariance intersection (CI). Simulation demonstrates the effectiveness of the proposed multi-sensor AA fusion-based $t$ filter in dealing with outliers as compared with the classic Gaussian estimator, and the advantage of the AA fusion in comparison with the CI approach and the augmented measurement fusion.

arXiv comment8 pages, 8 figures

References in corpus (5)

• Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion
• Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion
• Fusion of Probability Density Functions
• A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion
• Chernoff information of exponential families

Cited by in corpus (2)

• Arithmetic Average Density Fusion -- Part I: Some Statistic and Information-theoretic Results
• A Model Fusion Distributed Kalman Filter For Non-Gaussian Observation Noise