Abstract
In this letter, the 1-bit compressive sensing (CS) technology is utilized to recover joint sparse signal. In the context of multi-bits quantization, compared to the single measurement vector (SMV) case, the successful recovery rate can be significantly improved using multiple measurement vectors (MMV) model. Therefore, we introduce the MMV model for signal recovery from 1-bit sampling. Due to the amplitude information loss using 1-bit quantizing, a new sampling framework is proposed to estimate the ℓ2-norm of each measurement vector for continuous-time signal, followed by the corresponding estimation algorithm 1-bit iterative hard thresholding (1-bit IHT). Then we develop the algorithm of binary IHT for MMV (M-BIHT) to recover joint sparse signal under uncertain noise. Compared to the 1-bit SMV model and multi-bits quantization MMV mode, the experimental results illustrate the recovery performance is improved greatly by the 1-bit MMV model.
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