Radar Applications of Sparse Reconstruction and Compressed Sensing

2013 ◽  
pp. 147-208
Author(s):  
Parker ◽  
Potter ◽  
Ferrara
Keyword(s):  
Compressed Sensing ◽  
Sparse Reconstruction ◽  
Radar Applications

scholarly journals Compressed Sensing-Based Range-Doppler Processing Method for Passive Radar

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xia Bai ◽  
Hejing Guo ◽  
Juan Zhao ◽  
Tao Shan
Keyword(s):  
Compressed Sensing ◽  
Cross Correlation ◽  
Processing Method ◽  
Sparse Reconstruction ◽  
Digital Television ◽  
Experimental Measurements ◽  
Passive Radar ◽  
Computational Burden ◽  
Chinese Standard ◽  
Classical Cross

Passive radar (PR) systems use the existing transmitters of opportunity in the environment to perform tasks such as detection, tracking, and imaging. The classical cross-correlation based methods to obtain the range-Doppler map have the problems of high sidelobe and limited resolution due to the influence of signal bandwidth. In this paper, we propose a novel range-Doppler processing method based on compressed sensing (CS), which performs sparse reconstruction in range and Doppler dimensions to achieve high resolution and reduces sidelobe without excessive computational burden. Results from numerical simulations and experimental measurements recorded with the Chinese standard digital television terrestrial broadcasting (DTTB) based PR show that the proposed method successfully handles the range-Doppler map formatting problem for PR and outperforms the existing CS-based PR processing methods.

scholarly journals Sparse reconstruction with multiple Walsh matrices

2019 ◽  
Vol 10 (3) ◽  
pp. 221-239
Author(s):  
Enrico Au-Yeung
Keyword(s):  
Compressed Sensing ◽  
Dimensionality Reduction ◽  
Vector Space ◽  
Sparse Reconstruction ◽  
Dimensional Vector ◽  
Dimensional Vector Space ◽  
Computational Harmonic Analysis ◽  
Sparse Vector ◽  
Class Of Matrices ◽  
Lower Dimensional

Abstract The problem of how to find a sparse representation of a signal is an important one in applied and computational harmonic analysis. It is closely related to the problem of how to reconstruct a sparse vector from its projection in a much lower-dimensional vector space. This is the setting of compressed sensing, where the projection is given by a matrix with many more columns than rows. We introduce a class of random matrices that can be used to reconstruct sparse vectors in this paradigm. These matrices satisfy the restricted isometry property with overwhelming probability. We also discuss an application in dimensionality reduction where we initially discovered this class of matrices.

Medical Image Compressed Sensing Reconstruction

2014 ◽  
Vol 556-562 ◽  
pp. 4835-4838 ◽  
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu ◽  
Yu Ming Sun
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Medical Image ◽  
Reconstruction Algorithm ◽  
Gradient Projection ◽  
Sparse Reconstruction ◽  
Image Reconstruction Algorithm ◽  
Gradient Direction ◽  
Medical Image Reconstruction ◽  
Negative Gradient

In order to improve the speed of compressed sensing image reconstruction algorithm, a two step rapid gradient projection for sparse reconstruction in medical image reconstruction is proposed. in traditional gradient projection for sparse reconstruction algorithm, the searching direction is alternate between the negative gradient direction when the direction is ill, the searching speed is slow. Now we search with two step gradient projection, the speed is increased when meets the ill-condition. Compared with the original GPSR algorithm, the TSGPSR algorithm not only accelerate the speed of operation, but also improves the accuracy of the reconstruction. and exhibits higher robustness under different noise intensities.

scholarly journals Radar Array Diagnosis from Undersampled Data Using a Compressed Sensing/Sparse Recovery Technique

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
S. Costanzo ◽  
A. Borgia ◽  
G. Di Massa ◽  
D. Pinchera ◽  
M. D. Migliore
Keyword(s):  
Compressed Sensing ◽  
Sparse Recovery ◽  
Accurate Diagnosis ◽  
Small Set ◽  
Recovery Technique ◽  
Radar Applications ◽  
Experimental Validations ◽  
Slotted Waveguide ◽  
Recovery Approach ◽  
Undersampled Data

A Compressed Sensing/Sparse Recovery approach is adopted in this paper for the accurate diagnosis of fault array elements from undersampled data. Experimental validations on a slotted waveguide test array are discussed to demonstrate the effectiveness of the proposed procedure in the failures retrieval from a small set of measurements with respect to the number of radiating elements. Due to the sparsity feature of the proposed formulation, the method is particularly appealing for the diagnostics of large arrays, typically adopted for radar applications.

Adaptive Dual Conjudate Gradient Projection Algorithm for Compressed Sensing Image Reconstruction

2013 ◽  
Vol 347-350 ◽  
pp. 2600-2604
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Sparse Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Reconstruction Quality ◽  
Dual Gradient

In order to improve the quality of noise signals reconstruction method, an algorithm of adaptive dual gradient projection for sparse reconstruction of compressed sensing theory is proposed. In ADGPSR algorithm, the pursuit direction is updated in two conjudate directions, the better original signals estimated value is computed by conjudate coefficient. Thus the reconstruction quality is improved. Experiment results show that, compared with the GPSR algorithm, the ADGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.

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