Multisensor Suboptimal Fusion Student's $t$ Filter

نویسندگان

چکیده

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 single-sensor Kalman to setup based on suboptimal arithmetic average (AA) approach which driven from information-theoretic density optimization able deal with unknown correlation among sensors. To ensure computationally efficient, closed-form recursion, moment matching approximation has been used averaging densities aggregated different Based same framework, we also extend covariance intersection (CI) fusion. Simulation demonstrates strength AA fusion-based dealing outliers as compared classic Gaussian estimator, advantage comparison CI augmented

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ژورنال

عنوان ژورنال: IEEE Transactions on Aerospace and Electronic Systems

سال: 2023

ISSN: ['1557-9603', '0018-9251', '2371-9877']

DOI: https://doi.org/10.1109/taes.2022.3210157