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• W2775403132 abstract "In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters are simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters." @default.
• W2775403132 created "2017-12-22" @default.
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• W2775403132 date "2019-10-01" @default.
• W2775403132 modified "2023-10-14" @default.
• W2775403132 title "Robust Kalman Filters Based on Gaussian Scale Mixture Distributions With Application to Target Tracking" @default.
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• W2775403132 doi "https://doi.org/10.1109/tsmc.2017.2778269" @default.
• W2775403132 hasPublicationYear "2019" @default.
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