Compress Sensing
A. Vakili; M. Shams Esfand Abadi; M. Kalantari
Abstract
Background and Objectives: In the realm of compressed sensing, most greedy sparse recovery algorithms necessitate former information about the signal's sparsity level, which may not be available in practical conditions. To address this, methods based on the Sparsity Adaptive Matching Pursuit (SAMP) algorithm ...
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Background and Objectives: In the realm of compressed sensing, most greedy sparse recovery algorithms necessitate former information about the signal's sparsity level, which may not be available in practical conditions. To address this, methods based on the Sparsity Adaptive Matching Pursuit (SAMP) algorithm have been developed to self-determine this parameter and recover the signal using only the sampling matrix and measurements. Determining a suitable Initial Value for the algorithm can greatly affect the performance of the algorithm.Methods: One of the latest sparsity adaptive methods is Correlation Calculation SAMP (CCSAMP), which relies on correlation calculations between the signals recovered from the support set and the candidate set. In this paper, we present a modified version of CCSAMP that incorporates a pre-estimation phase for determining the initial value of the sparsity level, as well as a modified acceptance criteria considering the variance of noise. Results: To validate the efficiency of the proposed algorithm over the previous approaches, random sparse test signals with various sparsity levels were generated, sampled at the compression ratio of 50%, and recovered with the proposed and previous methos. The results indicate that the suggested method needs, on average, 5 to 6 fewer iterations compared to the previous methods, just due to the pre-estimation of the initial guess for the sparsity level. Furthermore, as far as the least square technique is integrated in some parts of the algorithm, in presence of noise the modified acceptance criteria significantly improve the success rate while achieving a lower mean squared error (MSE) in the recovery process.Conclusion: The pre-estimation process makes it possible to recover signal with fewer iterations while keeping the recovery quality as before. The fewer the number of iterations, the faster the algorithm. By incorporating the noise variance into the accept criteria, the method achieves a higher success rate and a lower mean squared error (MSE) in the recovery process.
Compress Sensing
M. Kalantari
Abstract
Background and Objectives: Compressed sensing (CS) of analog signals in shift-invariant spaces can be used to reduce the complexity of the matched-filter (MF) receiver, in which we can be approached the standard MF performance with fewer filters. But, with a small number of filters the performance degrades ...
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Background and Objectives: Compressed sensing (CS) of analog signals in shift-invariant spaces can be used to reduce the complexity of the matched-filter (MF) receiver, in which we can be approached the standard MF performance with fewer filters. But, with a small number of filters the performance degrades quite rapidly as a function of SNR. In fact, the CS matrix aliases all the noise components, therefore the noise increases in the compressed measurements. This effect is referred to as noise folding. In this paper, an approach for compensating the noise folding effect is proposed. Methods: An approach for compensating of this effect is to use a sufficient number of filters. In this paper the aim is to reach the better performance with the same number of filter as in the previous work. This, can be approached using a weighting function embedded in the analog signal compressed sensing structure. In fact, using this weighting function we can remedy the effect of CS matrix on the noise variance. Results: Comparing with the approach based on using the sufficient number of filters to counterbalance the noise increase, experimental results show that with the same numbers of filters, in terms of probability of correct detection, the proposed approach remarkably outperforms the rival’s.Conclusion: Noise folding formation is the main factor in CS-based matched-filter receiver. The method previously presented to reduce this effect demanded using the sufficient number of filters which comes at a cost. In this paper we propose a new method based on using the weighting function embedded in the analog signal compressed sensing structure to achieve better performance.
Compress Sensing
Z. Habibi; H. Zayyani; M. Shams Esfandabadi
Abstract
Background and Objectives: Compressive sensing (CS) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-sparse systems. We can follow this issue, by using CS theory and block-sparse ...
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Background and Objectives: Compressive sensing (CS) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-sparse systems. We can follow this issue, by using CS theory and block-sparse signal recovery algorithms.Methods: This paper presents a new block-sparse signal recovery algorithm for the adaptive block-sparse system identification scenario, named stochastic block normalized iterative hard thresholding (SBNIHT) algorithm. The proposed algorithm is a new block version of the SSR normalized iterative hard thresholding (NIHT) algorithm with an adaptive filter framework. It uses a search method to identify the blocks of the impulse response of the unknown block-sparse system that we wish to estimate. In addition, the necessary condition to guarantee the convergence for this algorithm is derived in this paper.Results: Simulation results show that the proposed SBNIHT algorithm has a better performance than other algorithms in the literature with respect to the convergence and tracking capability.Conclusion: In this study, one new greedy algorithm is suggested for the block-sparse system identification scenario. Although the proposed SBNIHT algorithm is more complex than other competing algorithms but has better convergence and tracking capability performance.
