Sparsity-fused Kalman filtering for reconstruction of dynamic sparse signals
IEEE International Conference on Communications
2015 IEEE International Conference on Signal Processing for Communications (ICC)
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Ding, X., Chen, W., & Wassell, I. (2015). Sparsity-fused Kalman filtering for reconstruction of dynamic sparse signals. IEEE International Conference on Communications https://doi.org/10.1109/ICC.2015.7249389
This article focuses on the problem of reconstructing dynamic sparse signals from a series of noisy compressive sensing measurements using a Kalman Filter (KF). This problem arises in many applications, e.g., Magnetic Resonance Imaging (MRI), Wireless Sensor Networks (WSN) and video reconstruction. The conventional KF does not consider the sparsity structure presented in most practical signals and it is therefore inaccurate when being applied to sparse signal recovery. To deal with this issue, we derive a novel KF procedure which takes the sparsity model into consideration. Furthermore, an algorithm, namely Sparsity-fused KF, is proposed based upon it. The method of iterative soft thresholding is utilized to refine our sparsity model. The superiority of our method is demonstrated by synthetic data and the practical data gathered by a WSN.
This work is supported by EPSRC Research Grant (EP/K033700/1); the Natural Science Foundation of China (61401018, U1334202); the State Key Laboratory of Rail Traffic Control and Safety (RCS2014ZT08), Beijing Jiaotong University; the Fundamental Research Funds for the Central Universities (2014JBM149); the Key Grant Project of Chinese Ministry of Education (313006); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.
Engineering and Physical Sciences Research Council (EP/K033700/1)
External DOI: https://doi.org/10.1109/ICC.2015.7249389
This record's URL: https://www.repository.cam.ac.uk/handle/1810/251194