2019/02/15 by Giorgio M. Vitetta, Vitetta, Giorgio M., Pasquale Di Viesti +3 · 1 citation
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Computation (stat.CO) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Target Tracking and Data Fusion in Sensor Networks #stat.CO
paper · pdf · doi:10.48550/arxiv.1902.05717
arxiv created 2019/02/15 · openalex publication_date 2019/02/15 · arxiv updated 2019/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, a novel method for developing filtering algorithms, based on the parallel concatenation of Bayesian filters and called turbo filtering, has been proposed. In this manuscript we show how the same conceptual approach can be exploited to devise a new smoothing method, called turbo smoothing. A turbo smoother combines a turbo filter, employed in its forward pass, with the parallel concatenation of two backward information filters used in its backward pass. As a specific application of our general theory, a detailed derivation of two turbo smoothing algorithms for conditionally linear Gaussian systems is illustrated. Numerical results for a specific dynamic system evidence that these algorithms can achieve a better complexity-accuracy tradeoff than other smoothing techniques recently appeared in the literature.