Research On 1-Bit Compressed Sensing Reconstruction Algorithm

Compressive sensing is a hot topic in the field of signal acquisition and processing in recent years.As an important branch of compressive sensing theory,1-bit compressive sensing quantizes each measurement to one bit,which simplifies the hardware structure extremely and improves the quantizer's efficiency.This paper studies the reconstruction algorithm under the 1-bit compressive sensing model,the specific research content is as follows:Firstly,in order to solve the problem of low reconstruction performance and poor convergence performance in the binary iterative reweighted algorithm,this paper proposed a based on approximate message passing binary iterative reweighted algorithm.This algorithm introduces the Onsager term which belongs to the approximate message passing algorithm in binary iterative reweighted algorithm,which realizes the effectively reconstruct under the condition of the unknown signal sparsity,and also improves the convergence speed and the reconstruction performance of the algorithm.Then,in order to solve the problem that the pinball iterative hard threshold(PIHT)algorithm can not accurately reconstructed in the case of unknown signal sparsity,an sparsity adaptive pinball iterative hard threshold(SAPIHT)algorithm is proposed.This algorithm inherits the advantages of PIHT algorithm and introduces the sparsity adaptive technology,which can effectively reconstruct the original signal when the sparsity of the original signal is unknown in the noisy environment.Finally,in order to solve the problem of the quantization threshold is a fixed value of zero in binary iterative hard threshold(BIHT)algorithm,that generate quantization error is easily,this paper proposed an adaptive thresholding-based binary iteration hard thresholding(AT-BIHT)algorithm.This algorithm uses the adaptive thresholding-based binary quantizer instead of the symbolic function in BIHT.It not only inherits the advantages of BIHT,but also improves the reconstruction performance efficiently.In this paper adaptive thresholding-based binary iteration hard thresholding algorithm canachieve significantly higher reconstruction performance than binary iterative hard threshold algorithm.