VAMP-Based Kalman Filtering Under Non-Gaussian Process Noise

VAMP-Based Kalman Filtering Under Non-Gaussian Process Noise

Estimating time-varying signals becomes particularly challenging in the face of non-Gaussian (e.g., sparse) and/or rapidly time-varying process noise. By building upon the recent progress in the approximate message passing (AMP) paradigm, this paper unifies the vector variant of AMP (i.e., VAMP) wit...

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Bibliographic Details
Main Authors: Tiancheng Gao, Mohamed Akrout, Faouzi Bellili, Amine Mezghani
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of Signal Processing
Subjects:
Bayesian inference
expectation propagation
vector approximate message passing (VAMP)
Kalman filter
time-varying signals
rapidly sparse process noise
Online Access:https://ieeexplore.ieee.org/document/10947573/
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