Parallel reconstruction method for joint sparse vector based on convolutional deep stacking network

Abstract

The invention belongs to the technical field of compressed sensing of a multi-measurement vector model, and specifically relates to a parallel reconstruction method for a joint sparse vector based ona convolutional deep stacking network. Inspired by the successful application of a deep learning technology to a pattern classification problem, the parallel reconstruction method acquires a joint sparse structure of channel signals by using the convolutional deep stacking network, converts an atom selection problem into an atom classification problem and selects a plurality of candidate atoms ateach iteration, thereby effectively solving a problem of signal reconstruction under the multi-measurement vector model, and keeping low complexity of the algorithm.

Description

technical field [0001] The invention belongs to the technical field of compressed sensing of multi-measurement vector models, and in particular relates to a method for parallel reconstruction of joint sparse vectors based on convolution depth stacking network. Background technique [0002] With the rapid development of information technology, society has entered the era of big data with information explosion, and massive information sampling, transmission and storage have brought unprecedented pressure and challenges to existing technologies. The traditional signal processing technology mainly includes four steps of high-rate sampling, transformation, compression, and reconstruction. Compression is realized by retaining larger transform base coefficients and setting other transform base coefficients to zero. This high-speed sampling and recompression process wastes a lot of sampling resources, especially in the case of high sampling cost, large sampling data and complex samp...

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Problems solved by technology

However, such algorithms are affected by the amount of calculation of sparse Bayes

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