scholarly journals MIMO Radar Imaging Based on Smoothedl0Norm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jun-Jie Feng ◽  
Gong Zhang ◽  
Fang-Qing Wen
Keyword(s):  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Radar Imaging ◽  
Mimo Radar ◽  
Hyperbolic Tangent ◽  
Imaging Algorithm ◽  
Two Dimensional Image ◽  
Tangent Function

For radar imaging, a target usually has only a few strong scatterers which are sparsely distributed. In this paper, we propose a compressive sensing MIMO radar imaging algorithm based on smoothedl0norm. An approximate hyperbolic tangent function is proposed as the smoothed function to measure the sparsity. A revised Newton method is used to solve the optimization problem by deriving the new revised Newton directions for the sequence of approximate hyperbolic tangent functions. In order to improve robustness of the imaging algorithm, main value weighted method is proposed. Simulation results show that the proposed algorithm is superior to Orthogonal Matching Pursuit (OMP), smoothedl0method (SL0), and Bayesian method with Laplace prior in performance of sparse signal reconstruction. Two-dimensional image quality of MIMO radar using the new method has great improvement comparing with aforementioned reconstruction algorithm.

An Optimised Signal Reconstruction Algorithm for Compressed Sensing

2013 ◽  
Vol 333-335 ◽  
pp. 567-571
Author(s):  
Zhao Shan Wang ◽  
Shan Xiang Lv ◽  
Jiu Chao Feng ◽  
Yan Sheng ◽  
Zhong Liang Wu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Small Proportion ◽  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Orthogonal Matching Pursuit ◽  
Signal Recovery ◽  
Successful Recovery ◽  
Stagewise Orthogonal Matching Pursuit ◽  
Convergence Of Algorithm

Signal recovery is a key issue in compressed sensing field. A new greedy reconstruction algorithm termed Optimised Stagewise Orthogonal Matching Pursuit (OSOMP) is proposed, which is an improved version for Stagewise Orthogonal Matching Pursuit (StOMP). In preselection step, OSOMP chooses several coordinates with a calculated threshold to accelerate the convergence of algorithm. In following pruning step, a small proportion of selected coordinates are discarded according to the amplitude of estimated signal, thus most false discovered coordinates can be swept away. Experimental results show that in OSOMP, the scale of estimated support can be controlled very well, and the successful recovery rate is also much higher than that in StOMP.

Group sparsity based imaging algorithm for TWRI under wall parameter uncertainties

2016 ◽  
Vol 40 (1) ◽  
pp. 251-260 ◽  
Author(s):  
Yanpeng Sun ◽  
Shi Zhang ◽  
Zhao Cui
Keyword(s):  
High Resolution ◽  
Matching Pursuit ◽  
Multipath Propagation ◽  
Reconstruction Algorithm ◽  
Imaging Method ◽  
Group Sparsity ◽  
Parameter Uncertainties ◽  
Reconstruction Procedure ◽  
Imaging Algorithm ◽  
Imaging Algorithms

Through-the-wall radar imaging (TWRI) applications allow accurate target localization and high-resolution imaging. However, multipath propagation generates challenges to the image reconstruction procedure. With distortions in the received radar signals, traditional imaging algorithms are not able to acquire high-resolution images. The unpredictability of the indoor scattering environment makes this even worse. In this paper, a novel block orthogonal matching pursuit (BOMP)-based group-sparsity reconstruction algorithm combined with particle swarm optimization (PSO) is proposed for reliable scene reconstruction. The proposed imaging algorithm can recover the image of targets by exploiting multipath propagation and simultaneously estimating wall parameters with high accuracy. The effectiveness of the proposed imaging method has been further demonstrated via simulation results.

scholarly journals On a Sparse Approximation of Compressible Signals

2019 ◽  
Vol 39 (4) ◽  
pp. 2232-2243 ◽  
Author(s):  
Grzegorz Dziwoki ◽  
Marcin Kucharczyk
Keyword(s):  
Matching Pursuit ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Sparse Approximation ◽  
Residual Error ◽  
Practical Reasons ◽  
Reconstruction Procedure ◽  
Approximation Process ◽  
Approximation Quality ◽  
Fine Control

Abstract Many physical phenomena can be modeled by compressible signals, i.e., the signals with rapidly declining sample amplitudes. Although all the samples are usually nonzero, due to practical reasons such signals are attempted to be approximated as sparse ones. Because sparsity of compressible signals cannot be unambiguously determined, a decision about a particular sparse representation is often a result of comparison between a residual error energy of a reconstruction algorithm and some quality measure. The paper explores a relation between mean square error (MSE) of the recovered signal and the residual error. A novel, practical solution that controls the sparse approximation quality using a target MSE value is the result of these considerations. The solution was tested in numerical experiments using orthogonal matching pursuit (OMP) algorithm as the signal reconstruction procedure. The obtained results show that the proposed quality metric provides fine control over the approximation process of the compressible signals in the mean sense even though it has not been directly designed for use in compressed sensing methods such as OMP.

scholarly journals Cross Validation Based Distributed Greedy Sparse Recovery for Multiview Through-the-Wall Radar Imaging

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Lele Qu ◽  
Shimiao An ◽  
Yanpeng Sun
Keyword(s):  
Cross Validation ◽  
Matching Pursuit ◽  
Measurement Data ◽  
Radar Imaging ◽  
Sparse Recovery ◽  
Imaging Algorithm ◽  
Recovery Algorithm ◽  
Recovery Error ◽  
Cv Measurements ◽  
Imaging Performance

Multiview through-the-wall radar imaging (TWRI) can improve the imaging quality and target detection by exploiting the measurement data acquired from various views. Based on the established joint sparsity signal model for multiview TWRI, a cross validation (CV) based distributed greedy sparse recovery algorithm which combines the strengths of the CV technique and censored simultaneous orthogonal matching pursuit algorithm (CSOMP) is proposed in this paper. The developed imaging algorithm named by CV-CSOMP which separates the total measurements into reconstruction measurements and CV measurements is able to achieve the accurate imaging reconstruction and estimation of recovery error tolerance by the iterative CSOMP calculation. The proposed CV-CSOMP imaging algorithm not only can reduce the communication costs among radar units, but also can provide the desirable imaging performance without the prior information such as the sparsity or noise level. The experimental results have verified the validity and effectiveness of the proposed imaging algorithm.

Bayesian Matching Pursuit-Based Distributed FMCW MIMO Radar Imaging

2020 ◽  
pp. 1-12
Author(s):  
Jiho Seo ◽  
SeongJun Hwang ◽  
Yong-gi Hong ◽  
Jaehyun Park ◽  
Sunghyun Hwang ◽  
...  
Keyword(s):  
Matching Pursuit ◽  
Radar Imaging ◽  
Mimo Radar

scholarly journals Sparse Signal Reconstruction Based on Multiparameter Approximation Function with Smoothedl0Norm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao-Feng Fang ◽  
Jiang-She Zhang ◽  
Ying-Qi Li
Keyword(s):  
Optimization Problem ◽  
Null Space ◽  
Signal Reconstruction ◽  
Iteration Process ◽  
Reconstruction Algorithm ◽  
Measurement Matrix ◽  
Hyperbolic Tangent ◽  
Reconstruction Quality ◽  
And Performance ◽  
Quasi Newton

The smoothedl0norm algorithm is a reconstruction algorithm in compressive sensing based on approximate smoothedl0norm. It introduces a sequence of smoothed functions to approximate thel0norm and approaches the solution using the specific iteration process with the steepest method. In order to choose an appropriate sequence of smoothed function and solve the optimization problem effectively, we employ approximate hyperbolic tangent multiparameter function as the approximation to the big “steep nature” inl0norm. Simultaneously, we propose an algorithm based on minimizing a reweighted approximatel0norm in the null space of the measurement matrix. The unconstrained optimization involved is performed by using a modified quasi-Newton algorithm. The numerical simulation results show that the proposed algorithms yield improved signal reconstruction quality and performance.

PERBANDINGAN METODE HOMOMORPHIC FILTERING DAN METODE CONTRAST STRECHING UNTUK PERBAIKAN KUALITAS CITRA UNDERWATER

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Adi Mora Lubis ◽  
Nelly Astuti Hasibuan ◽  
Imam Saputra
Keyword(s):  
Digital Image ◽  
Water Environment ◽  
Repair Process ◽  
Image Process ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Underwater Image ◽  
Homomorphic Filtering ◽  
Contrast Stretching

Digital imagery is a two-dimensional image process through a digital computer that is used to manipulate and modify images in various ways. Photos are examples of two-dimensional images that can be processed easily. Each photo in the form of a digital image can be processed through a specific software. In the water environment, the light factor greatly influences the results of the quality of the image obtained. With the deepening of underwater shooting, the results obtained will be the darker the quality of the underwater image. . uneven lighting and bluish tones. One of the factors that influence the recognition results in pattern recognition is the quality of the image that is inputted. The image acquired from the source does not always have good quality. The process of repairing digital images that experience interference in lighting. The lighting repair process uses homomorphic filtering and uses contrast striching and will compare the quality of both methods and test to prove the results of image quality between homomorphic filtering and contrast streching. Until later the results of both methods can be seen which is better. homomorphic filtering and contrast stretching can produce image improvements with pretty good performance.Keywords: Digital Image, Underwater Image, Homomorphic Filtering, Contrast Streching, Matlab R2010a

IMPLEMENTASI METODE RETINEX UNTUK MENINGKATKAN KUALITAS CITRA UNDERWATER

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Bainun Harahap
Keyword(s):  
Water Environment ◽  
Repair Process ◽  
Image Process ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Underwater Image ◽  
Object Search ◽  
Image Improvement ◽  
Underwater Object

Digital imagery is a two-dimensional image process through a digital computer that is used to manipulate and modify images in various ways. Photos are examples of two-dimensional images that can be processed easily. Each photo in the form of a digital image can be processed through certain software devices. In the water environment, light factors greatly influence the results of image quality obtained. With the deepening of underwater shooting, the results obtained will be the darker the quality of the underwater image. Underwater imagery is widely used as an object in various activities such as underwater habitat mapping, underwater environment monitoring, underwater object search. Uneven lighting and colors that tend to be bluish and runny. One of the factors that influence the recognition results in pattern recognition is the quality of the image that is inputted. The image acquired from the source does not always have good quality. The process of improvement in digital images that experience interference in lighting and exposure to sunlight. The lighting repair process uses the retinex method and will compare the quality of the two methods later. Until later the results of both methods can be seen which is better. Retinex method can produce image improvement with high performance.Keywords: Digital Cintra, Underwater, Matlab Retinex Method

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