scholarly journals Compressed Sensing/Sparse-Recovery Approach for Improved Range Resolution in Narrow-Band Radar

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Sandra Costanzo
Keyword(s):  
Compressed Sensing ◽  
Narrow Band ◽  
Mathematical Formulation ◽  
Sparse Recovery ◽  
Range Resolution ◽  
Biological Objects ◽  
Resolution Capability ◽  
Experimental Validations ◽  
Recovery Approach ◽  
Recovery Procedure

A compressed sensing/sparse-recovery procedure is adopted to obtain enhanced range resolution capability from the processing of data acquired with narrow-band SFCW radars. A mathematical formulation for the proposed approach is reported and validity limitations are fully discussed, by demonstrating the ability to identify a great number of targets, up to 20, in the range direction. Both numerical and experimental validations are presented, by assuming also noise conditions. The proposed method can be usefully applied for the accurate detection of parameters with very small variations, such as those involved in the monitoring of soil deformations or biological objects.

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.

scholarly journals Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 834
Author(s):  
Jin ◽  
Yang ◽  
Li ◽  
Liu
Keyword(s):  
Compressed Sensing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Sparse Recovery ◽  
Coordinate Descent ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

scholarly journals A Fast Sparse Recovery Algorithm for Compressed Sensing Using Approximate l0 Norm and Modified Newton Method

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1227 ◽  
Author(s):  
Dingfei Jin ◽  
Yue Yang ◽  
Tao Ge ◽  
Daole Wu
Keyword(s):  
Compressed Sensing ◽  
Newton Method ◽  
Simulation Experiment ◽  
Sparse Recovery ◽  
Signal Recovery ◽  
Recovery Efficiency ◽  
Recovery Algorithm ◽  
Modified Newton Method ◽  
Computer Simulation Experiment ◽  
Recovery Algorithms

In this paper, we propose a fast sparse recovery algorithm based on the approximate l0 norm (FAL0), which is helpful in improving the practicability of the compressed sensing theory. We adopt a simple function that is continuous and differentiable to approximate the l0 norm. With the aim of minimizing the l0 norm, we derive a sparse recovery algorithm using the modified Newton method. In addition, we neglect the zero elements in the process of computing, which greatly reduces the amount of computation. In a computer simulation experiment, we test the image denoising and signal recovery performance of the different sparse recovery algorithms. The results show that the convergence rate of this method is faster, and it achieves nearly the same accuracy as other algorithms, improving the signal recovery efficiency under the same conditions.

Nonuniform recovery of fusion frame structured sparse signals

2017 ◽  
Vol 15 (03) ◽  
pp. 333-352
Author(s):  
Yu Xia ◽  
Song Li
Keyword(s):  
Probability Distribution ◽  
Compressed Sensing ◽  
Fourier Coefficients ◽  
A Priori ◽  
Sparse Recovery ◽  
A Priori Knowledge ◽  
Sparse Signals ◽  
Fusion Frame ◽  
Redundant Representation ◽  
Vector Valued

This paper considers the nonuniform sparse recovery of block signals in a fusion frame, which is a collection of subspaces that provides redundant representation of signal spaces. Combined with specific fusion frame, the sensing mechanism selects block-vector-valued measurements independently at random from a probability distribution [Formula: see text]. If the probability distribution [Formula: see text] obeys a simple incoherence property and an isotropy property, we can faithfully recover approximately block sparse signals via mixed [Formula: see text]-minimization in ways similar to Compressed Sensing. The number of measurements is significantly reduced by a priori knowledge of a certain incoherence parameter [Formula: see text] associated with the angles between the fusion frame subspaces. As an example, the paper shows that an [Formula: see text]-sparse block signal can be exactly recovered from about [Formula: see text] Fourier coefficients combined with fusion frame [Formula: see text], where [Formula: see text].

Narrow-band radar imaging for off-grid spinning targets via compressed sensing

2016 ◽  
Vol 28 (4) ◽  
pp. 1167-1181 ◽  
Author(s):  
Chao Sun ◽  
Baoping Wang ◽  
Yang Fang ◽  
Zuxun Song
Keyword(s):  
Compressed Sensing ◽  
Narrow Band ◽  
Radar Imaging ◽  
Spinning Targets

scholarly journals Parametric Narrow-Band InISAR 3D Imaging Based on Compressed Sensing

2020 ◽  
Vol 29 (3) ◽  
pp. 508-514
Author(s):  
Baoping WANG ◽  
Yan ZHANG ◽  
Yang FANG ◽  
Zuxun SONG
Keyword(s):  
Compressed Sensing ◽  
3D Imaging ◽  
Narrow Band

scholarly journals Joint Frame Synchronization and Channel Estimation: Sparse Recovery Approach and USRP Implementation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39041-39053 ◽  
Author(s):  
Ozgur Ozdemir ◽  
Chethan Kumar Anjinappa ◽  
Ridha Hamila ◽  
Naofal Al-Dhahir ◽  
Ismail Guvenc
Keyword(s):  
Channel Estimation ◽  
Sparse Recovery ◽  
Frame Synchronization ◽  
Recovery Approach

Studying Optical Characteristics of Diffused Light Reflecting from Naturally Senescing Leaves of Deciduous Trees

2020 ◽  
pp. 72-82
Author(s):  
Yu.V. Mamelin ◽  
G.F. Kopytov ◽  
V.Yu. Buzko
Keyword(s):  
Narrow Band ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Optical Characteristics ◽  
Light Reflection ◽  
Deciduous Trees ◽  
Biological Objects ◽  
Light Reflectance ◽  
Diffused Light ◽  
First Time

Study object included leaves of deciduous trees in the Krasnodar Territory at different stages of senescing visually manifested in their color alteration. Study subject was the optical characteristics of light diffused reflection from green, yellow-green and yellow leaves of deciduous trees in the Krasnodar Territory during the autumn season. Work objective lies in identifying the possibility to establish differences between green leaves of deciduous trees, and yellow-green and yellow leaves of deciduous trees using the terrain multispectral and hyperspectral sounding methods, as well as collecting information on spectral characteristics of the diffused light being reflected from various biological objects. Results of quantitative and qualitative analysis of data obtained through the diffused light reflectance spectroscopy from leaves of deciduous trees are presented. Narrow-band vegetation indices mNDVI705, mSR705, CRI1, SIPI and PSRI were used in quantitative analysis of data on the diffused light reflection spectra obtained from green, yellow-green and yellow leaves of deciduous trees. It was revealed that the use of narrow-band vegetation indices in the remote sensing algorithms using multi- and hyperspectral cameras makes it possible to rather accurately distinguish leaves at different stages of senescing. Optical characteristics of diffused light reflection from green, yellow-green and yellow leaves of deciduous trees, which are typical species of trees in urban and rural plantings in the Krasnodar Territory, are described for the first time

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