Analyzes likelihood-constrained adversarial observation shifts and their effects on latent states and policies in linear probabilistic SSMs used for RL.
Robust Bayesian Filtering and Smoothing Using Student's t Distribution
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abstract
State estimation in heavy-tailed process and measurement noise is an important challenge that must be addressed in, e.g., tracking scenarios with agile targets and outlier-corrupted measurements. The performance of the Kalman filter (KF) can deteriorate in such applications because of the close relation to the Gaussian distribution. Therefore, this paper describes the use of Student's t distribution to develop robust, scalable, and simple filtering and smoothing algorithms. After a discussion of Student's t distribution, exact filtering in linear state-space models with t noise is analyzed. Intermediate approximation steps are used to arrive at filtering and smoothing algorithms that closely resemble the KF and the Rauch-Tung-Striebel (RTS) smoother except for a nonlinear measurement-dependent matrix update. The required approximations are discussed and an undesirable behavior of moment matching for t densities is revealed. A favorable approximation based on minimization of the Kullback-Leibler divergence is presented. Because of its relation to the KF, some properties and algorithmic extensions are inherited by the t filter. Instructive simulation examples demonstrate the performance and robustness of the novel algorithms.
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stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning
Analyzes likelihood-constrained adversarial observation shifts and their effects on latent states and policies in linear probabilistic SSMs used for RL.