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compressed-sensing--cs
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Successive concave sparsity approximation for compressed sensing
, Article IEEE Transactions on Signal Processing ; Volume 64, Issue 21 , 2016 , Pages 5657-5671 ; 1053587X (ISSN) ; Koochakzadeh, A ; Babaie Zadeh, M ; Jansson, M ; Rojas, C. R ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2016
Abstract
In this paper, based on a successively accuracy-increasing approximation of the ℓ0 norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measurements. The approximations are realized with a certain class of concave functions that aggressively induce sparsity and their closeness to the ℓ0 norm can be controlled. We prove that the series of the approximations asymptotically coincides with the ℓ1 and ℓ0 norms when the approximation accuracy changes from the worst fitting to the best fitting. When measurements are noise-free, an optimization scheme is proposed that leads to a number of weighted ℓ1 minimization programs, whereas, in the presence of noise, we propose...
Sparse decomposition over non-full-rank dictionaries
, Article 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, 19 April 2009 through 24 April 2009 ; 2009 , Pages 2953-2956 ; 15206149 (ISSN); 9781424423545 (ISBN) ; Vigneron, V ; Jutten, C ; Institute of Electrical and Electronics Engineers; Signal Processing Society ; Sharif University of Technology
2009
Abstract
Sparse Decomposition (SD) of a signal on an overcomplete dictionary has recently attracted a lot of interest in signal processing and statistics, because of its potential application in many different areas including Compressive Sensing (CS). However, in the current literature, the dictionary matrix has generally been assumed to be of full-rank. In this paper, we consider non-full-rank dictionaries (which are not even necessarily overcomplete), and extend the definition of SD over these dictionaries. Moreover, we present an approach which enables to use previously developed SD algorithms for this non-full-rank case. Besides this general approach, for the special case of the Smoothed ℓ0 (SL0)...
On the error of estimating the sparsest solution of underdetermined linear systems
, Article IEEE Transactions on Information Theory ; Volume 57, Issue 12 , December , 2011 , Pages 7840-7855 ; 00189448 (ISSN) ; Jutten, C ; Mohimani, H ; Sharif University of Technology
Abstract
Let A be an n × m matrix with m > n, and suppose that the underdetermined linear system As = x admits a sparse solution ∥s 0∥o < 1/2spark(A). Such a sparse solution is unique due to a well-known uniqueness theorem. Suppose now that we have somehow a solution ŝ as an estimation of s0, and suppose that ŝ is only "approximately sparse", that is, many of its components are very small and nearly zero, but not mathematically equal to zero. Is such a solution necessarily close to the true sparsest solution? More generally, is it possible to construct an upper bound on the estimation error ∥ŝ - s 0∥2 without knowing s0? The answer is positive, and in this paper, we construct such a bound based on...
Off-grid localization in mimo radars using sparsity
, Article IEEE Signal Processing Letters ; Volume 25, Issue 2 , 2018 , Pages 313-317 ; 10709908 (ISSN) ; Gazor, S ; Marvasti, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
In this letter, we propose a new accurate approach for target localization in multiple-input multiple-output (MIMO) radars, which exploits the sparse spatial distribution of targets to reduce the sampling rate. We express the received signal of a MIMO radar in terms of the deviations of target parameters from the grid points in the form of a block sparse signal using the expansion around all the neighbor points. Applying a block sparse recovery method, we can estimate both the grid-point locations of targets and these deviations. The proposed approach can yield more accurate localization with higher detection probability compared with its counterparts. Moreover, the proposed approach can...
Bayesian pursuit algorithm for sparse representation
, Article 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, 19 April 2009 through 24 April 2009 ; 2009 , Pages 1549-1552 ; 15206149 (ISSN); 9781424423545 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Institute of Electrical and Electronics Engineers; Signal Processing Society ; Sharif University of Technology
2009
Abstract
In this paper, we propose a Bayesian Pursuit algorithm for sparse representation. It uses both the simplicity of the pursuit algorithms and optimal Bayesian framework to determine active atoms in sparse representation of a signal. We show that using Bayesian Hypothesis testing to determine the active atoms from the correlations leads to an efficient activity measure. Simulation results show that our suggested algorithm has better performance among the algorithms which have been implemented in our simulations in most of the cases. ©2009 IEEE
An adaptive iterative thresholding algorithm for distributed mimo radars
, Article IEEE Transactions on Aerospace and Electronic Systems ; Volume 55, Issue 2 , 2019 , Pages 523-533 ; 00189251 (ISSN) ; Azghani, M ; Marvasti, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
In this paper, a Block Iterative Method with Adaptive Thresholding for Sparse Recovery (BIMATSR) is proposed to recover the received signal in an under-sampled distributed multiple-input multiple-output radar. The BIMATSR scheme induces block sparsity with the aid of a signal-dependent thresholding operator which increases the accuracy of the target parameter estimation task. We have proved that under some sufficient conditions, the suggested scheme converges to a stable solution. Moreover, different simulation scenarios confirm that the BIMATSR algorithm outperforms its counterparts in terms of the target parameter estimation. This superiority is achieved at the expense of slightly more...
Compressive sensing for elliptic localization in MIMO radars
, Article 24th Iranian Conference on Electrical Engineering, 10 May 2016 through 12 May 2016 ; 2016 , Pages 525-528 ; 9781467387897 (ISBN) ; Amiri, R ; Behnia, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
Abstract
In this paper, a sparsity-aware target localization method in multiple-input-multiple-output (MIMO) radars by utilizing time difference of arrival (TDOA) measurements is proposed. This method provides a maximum likelihood (ML) estimator for target position by employing compressive sensing techniques. Also, for fast convergence and mitigating the mismatch problem due to grid discretization, we address a block-based search coupled with an adaptive dictionary learning technique. The Cramer-Rao lower bound for this model is derived as a benchmark. Simulations results are included to verify the localization performance
1.5-D sparse array for millimeter-wave imaging based on compressive sensing techniques
, Article IEEE Transactions on Antennas and Propagation ; Volume 66, Issue 4 , April , 2018 , Pages 2008-2015 ; 0018926X (ISSN) ; Fakharzadeh, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
The goal of this paper is to reduce the antenna count in a millimeter (mm)-wave imaging system by proposing both hardware and software solutions. The concept of image sparsity in the transform domain is utilized to present the compressive sensing (CS) formulation for both mono-static and multistatic imaging at mm-wave frequencies. To reduce the complexity of the imaging system and reconstruction process, we introduce 1.5-D array structure, which is a random sparse array with orthogonal element locations. It is shown that the peak signal-to-noise ratio (PSNR) of the reconstructed image obtained by a 1.5-D array with 65% sparsity is very close to the PSNR of a uniform 2-D array for mono-static...
Feedback acquisition and reconstruction of spectrum-sparse signals by predictive level comparisons
, Article IEEE Signal Processing Letters ; Volume 25, Issue 4 , 2018 , Pages 496-500 ; 10709908 (ISSN) ; Gazor, S ; Rahnavard, N ; Marvasti, F ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based compressive sensing algorithms, our proposed algorithm gradually estimates and follows slow changes in the...
Antenna placement in a compressive sensing-based colocated mimo radar
, Article IEEE Transactions on Aerospace and Electronic Systems ; Volume 56, Issue 6 , 2020 , Pages 4606-4614 ; Amini, A ; Tohidi, E ; Bastani, M. H ; Leus, G ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Compressive sensing (CS) has been widely used in multiple-input-multiple-output (MIMO) radar in recent years. Unlike traditional MIMO radar, detection/estimation of targets in a CS-based MIMO radar is accomplished via sparse recovery. In this article, for a CS-based colocated MIMO radar with linear arrays, we attempt to improve the target detection performance by reducing the coherence of the associated sensing matrix. Our tool in reducing the coherence is the placement of the antennas across the array aperture. In particular, we choose antenna positions within a given grid. Initially, we formalize the position selection problem as finding binary weights for each of the locations. This...
A joint scheme of antenna placement and power allocation in a compressive-sensing-based colocated MIMO radar
, Article IEEE Sensors Letters ; Volume 6, Issue 10 , 2022 ; 24751472 (ISSN) ; Amini, A ; Amiri, R ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2022
Abstract
The spatial sparsity of targets in the radar scene is widely used in multiple-input multiple-output (MIMO) radar signal processing, either to improve the detection/estimation performance of the radar or to reduce the cost of the conventional MIMO radars (e.g., by reducing the number of antennas). While sparse target estimation is the main challenge in such an approach, here, we address the design of a compressive-sensing-based MIMO radar, which facilitates such estimations. In particular, we propose an efficient solution for the problem of joint power allocation and antenna placement based on minimizing the number of transmit antennas while constraining the coherence of the sensing matrix....