Multisensor Suboptimal Fusion Student's $t$ Filter

A multisensor fusion Student's $t$ filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. It extends the single-sensor Student's $t$ Kalman filter to the multisensor setup based on the suboptimal arithmetic average (AA) fusion approach which is driven from information-theoretic density fusion optimization and able to deal with unknown correlation among sensors. To ensure computationally efficient, closed-form $t$ density recursion, moment matching approximation has been used for averaging the $t$ densities aggregated from different sensors. Based on the same framework, we also extend the covariance intersection (CI) approach for $t$ density fusion. Simulation demonstrates the strength of the proposed multisensor 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.