DocumentCode :
239669
Title :
Continuous structure based Bayesian compressive sensing for sparse reconstruction of time-frequency distributions
Author :
Qisong Wu ; Zhang, Yimin D. ; Amin, Moeness G.
Author_Institution :
Center for Adv. Commun., Villanova Univ., Villanova, PA, USA
fYear :
2014
fDate :
20-23 Aug. 2014
Firstpage :
831
Lastpage :
836
Abstract :
In this paper, we propose a Bayesian compressive sensing algorithm for effective reconstruction of sparse signals that demonstrate sparsity as continuous but irregular narrow strips in a multi-dimensional space. Among many applications of this class of representations are the two-dimensional time-frequency distributions (TFDs) of radar signals, which are often modeled as frequency modulated (FM) signals characterized by their sparse and continuous instantaneous frequencies. A spike-and-slab prior is introduced to statistically encourage sparsity of the time-frequency representations (TFRs) across each segmented time-frequency region, and a patterned prior is imposed to enforce the continuous structure of the TFR. Compared with the existing sparse signal reconstruction techniques, the proposed technique achieves improved interpretation of the TFD, particularly when the signals are noisy or with missing samples.
Keywords :
Bayes methods; compressed sensing; signal reconstruction; time-frequency analysis; Bayesian compressive sensing algorithm; FM signals; TFD; TFR; continuous instantaneous frequency; continuous structure; frequency modulated signals; irregular narrow strips; multidimensional space; patterned prior; radar signals; segmented time-frequency region; sparse instantaneous frequency; sparse signals reconstruction techniques; spike-and-slab prior; time-frequency distributions; time-frequency representations; two-dimensional time-frequency distributions; Bayes methods; Digital signal processing; Frequency modulation; Matching pursuit algorithms; Signal processing algorithms; Time-frequency analysis; Bayesian compressive sensing; Time-frequency analysis; continuous structure; missing data sample; sparse reconstruction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Signal Processing (DSP), 2014 19th International Conference on
Conference_Location :
Hong Kong
Type :
conf
DOI :
10.1109/ICDSP.2014.6900783
Filename :
6900783
Link To Document :
بازگشت