Signal Processing: Image Communication, Volume (82), Year (2020-3) , Pages (115751-115761)

Title : ( Probabilistic Kalman filter for moving object tracking )

Authors: Fahime Farahi , Hadi Sadoghi Yazdi ,

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AMP is a low-cost iterative algorithm for recovering signal in compressed sensing. When the sampling matrix has IID zero-mean Gaussian elements, the convergence of AMP is analytically guaranteed. But for other sampling matrices, especially ill-conditioned matrices, the recovery performance of AMP degrades and even may be diverged. This problem limits the use of AMP in some applications such as imaging. In this paper, a method is proposed for modifying the AMP algorithm based on Bayesian theory for non-IID matrices. Simulation results show better robustness properties of the proposed algorithm for non-IID matrices in comparison with previous works. In other words, the proposed method has more precision in recovery, and converges with less iterations.


, Approximate message passing, compressed sensing, IID Gaussian matrices, low-rank product matrices, row orthogonal matrices
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author = {Farahi, Fahime and Sadoghi Yazdi, Hadi},
title = {Probabilistic Kalman filter for moving object tracking},
journal = {Signal Processing: Image Communication},
year = {2020},
volume = {82},
month = {March},
issn = {0923-5965},
pages = {115751--115761},
numpages = {10},
keywords = {Approximate message passing;compressed sensing;IID Gaussian matrices;low-rank product matrices;row orthogonal matrices},


%0 Journal Article
%T Probabilistic Kalman filter for moving object tracking
%A Farahi, Fahime
%A Sadoghi Yazdi, Hadi
%J Signal Processing: Image Communication
%@ 0923-5965
%D 2020