【摘 要】
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Signal reconstruction is a significantly important the-oretical issue for compressed sensing. Considering the situation of signal reconstruction with unknown sparsity, the conventional signal reconstruction algorithms usually perform low accuracy. In this
【机 构】
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College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Co
论文部分内容阅读
Signal reconstruction is a significantly important the-oretical issue for compressed sensing. Considering the situation of signal reconstruction with unknown sparsity, the conventional signal reconstruction algorithms usually perform low accuracy. In this work, a sparsity adaptive signal reconstruction algorithm us-ing sensing dictionary is proposed to achieve a lower recon-struction error. The sparsity estimation method is combined with the construction of the support set based on sensing dictionary. Using the adaptive sparsity method, an iterative signal recon-struction algorithm is proposed. The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory. According to a series of simulations, the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.
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