Wavelet-based Bayesian Algorithm for Distributed Compressed Sensing
عنوان مقاله: Wavelet-based Bayesian Algorithm for Distributed Compressed Sensing
شناسه ملی مقاله: JR_JIST-7-2_003
منتشر شده در شماره 2 دوره 7 فصل در سال 1398
شناسه ملی مقاله: JR_JIST-7-2_003
منتشر شده در شماره 2 دوره 7 فصل در سال 1398
مشخصات نویسندگان مقاله:
Razieh Torkamani - Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
Ramazan Ali Sadeghzadeh - Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
خلاصه مقاله:
Razieh Torkamani - Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
Ramazan Ali Sadeghzadeh - Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
The emerging field of compressive sensing enables the reconstruction of the signal from a small set of linear projections.
Traditional compressive sensing approaches deal with a single signal; while one can jointly reconstruct multiple signals via
distributed compressive sensing algorithm, which exploits both the inter- and intra-signal correlations via joint sparsity
models. Since the wavelet coefficients of many signals is sparse, in this paper, the wavelet transform is used as sparsifying
transform, and a new wavelet-based Bayesian distributed compressive sensing algorithm is proposed, which takes into
account the inter-scale dependencies among the wavelet coefficients via hidden Markov tree model, as well as the intersignal
correlations. This paper uses Bayesian procedure to statistically model this correlation via the prior distributions.
Also, in this work, a type-۱ joint sparsity model is used for jointly sparse signals, in which every sparse coefficient vector is
considered as the sum of a common component and an innovation component. In order to jointly reconstruct multiple
sparse signals, the centralized approach is used in distributed compressive sensing, in which all the data is processed in the
fusion center. Also, variational Bayes procedure is used to infer the posterior distributions of unknown variables.
Simulation results demonstrate that the structure exploited within the wavelet coefficients provides superior performance in
terms of average reconstruction error and structural similarity index.
کلمات کلیدی: Distributed Compressive Sensing; Joint Saprsity; Signal Reconstruction; Wavelet Transform; Hidden Markov
Tree Model; Variational Bayes
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1142411/