NDLI: Adaptive Kalman Filtered Compressive Sensing for Streaming Signals

Content Provider	IEEE Xplore Digital Library
Author	Hang Li Wenbin Guo Zhuo Sun Wenbo Wang
Copyright Year	2013
Description	Author affiliation: Wireless Signal Process. & Network Lab., Beijing Univ. of Posts & Telecommun. (BUPT), Beijing, China (Hang Li; Wenbin Guo; Zhuo Sun; Wenbo Wang)
Abstract	In this paper, we investigate the problem of utilizing the Kalman Filter to reconstruct signals with sparse frequency content under a streaming CS framework. We develop a Gaussian Markov model of the sparse streaming signal under the Analog-to-Information Converter (AIC) hardware structure and propose an adaptive Kalman Filter for the reconstruction. Different from existing CS schemes for streaming signals, we exploit the correlations between the signals of two consecutive observation windows to model the process in the state transition form so that the Kalman Filter can be incorporated to obtain the convergent estimation of the input streaming signal. Simulation experiments show the feasibility of the proposed model and demonstrate the superior performance of the proposed algorithms.
Starting Page	1
Ending Page	5
File Size	330202
Page Count	5
File Format	PDF
ISBN	9781467361873
ISSN	10903038
DOI	10.1109/VTCFall.2013.6692081
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kalman filters Estimation Vectors Adaptation models Signal to noise ratio Compressed sensing Markov processes
Content Type	Text
Resource Type	Article

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