Double Bayesian Smoothing as Message Passing

Abstract

Recently, a novel method for developing filtering algorithms, based on the interconnection of two Bayesian filters and called double Bayesian filtering, has been proposed. In this manuscript we show that the same conceptual approach can be exploited to devise a new smoothing method, called double Bayesian smoothing. A double Bayesian smoother combines a double Bayesian filter, employed in its forward pass, with the interconnection of two backward information filters used in its backward pass. As a specific application of our general method, a detailed derivation of double Bayesian smoothing algorithms for conditionally linear Gaussian systems is illustrated. Numerical results for two specific dynamic systems evidence that these algorithms can achieve a better complexity-accuracy tradeoff and tracking capability than other smoothing techniques recently appeared in the literature.

arXiv admin note: text overlap with arXiv:1902.05717 and arXiv:1907.01358

References in corpus (5)

• Sequential Monte Carlo smoothing for general state space hidden Markov models
• Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems
• Rao-Blackwellized particle smoothers for conditionally linear Gaussian models
• Multiple Bayesian Filtering as Message Passing
• Double Bayesian Smoothing as Message Passing

Cited by in corpus (2)

• Temporal Parallelization of Inference in Hidden Markov Models
• Double Bayesian Smoothing as Message Passing