2013/01/21 by Lukas Bolliger, Hans‐Andrea Loeliger, Hans-Andrea Loeliger +4
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Target Tracking and Data Fusion in Sensor Networks #cs.IT #math.IT
paper · pdf · doi:10.48550/arxiv.1301.4793
arxiv created 2013/01/21 · openalex publication_date 2013/01/21 · arxiv updated 2013/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The factor graph approach to discrete-time linear Gaussian state space models is well developed. The paper extends this approach to continuous-time linear systems/filters that are driven by white Gaussian noise. By Gaussian message passing, we then obtain MAP/MMSE/LMMSE estimates of the input signal, or of the state, or of the output signal from noisy observations of the output signal. These estimates may be obtained with arbitrary temporal resolution. The proposed input signal estimation does not seem to have appeared in the prior Kalman filtering literature.