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 An Industrial B-Mode Phased Array Ultrasonic Imaging Reconstruction Algorithm Based on FRI Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guangzhi Dai ◽  
Zhiyong He ◽  
Song Lin
Keyword(s):  
Phased Array ◽  
Simulation Experiment ◽  
Ultrasonic Imaging ◽  
Reconstruction Algorithm ◽  
Imaging Method ◽  
Sampling Point ◽  
Simple Circuit ◽  
Ultrasonic Signals ◽  
Imaging Algorithm ◽  
Imaging Reconstruction

Firstly, a novel FRI sampling model has been proposed according to the characteristics of ultrasonic signals. The model has the advantages such as good stability, strong antinoise ability, simple circuit implementation, and fewer preconditions, compared to the traditional methods. Then, in order to verify the validity of the sampling model, the method is applied to B-type ultrasonic imaging, and a B-type phased array ultrasonic imaging algorithm based on FRI sampling model is proposed. Finally, the algorithm simulation experiment is designed, and the results show that the sampling point required by the proposed FRI sampling model is only 0.1% of the traditional B-type phased array ultrasonic imaging method, and the sampling frequency of the proposed ultrasonic imaging algorithm is only 0.0077% of the traditional B-type ultrasonic imaging method. Additionally, the experiment result indicates that this algorithm is more applicable to phased array ultrasonic imaging than the SOS filter is.

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.

scholarly journals Group Sparse Basis Pursuit Denoising Reconstruction Algorithm for Polarimetric Through-the-Wall Radar Imaging

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lele Qu ◽  
Shimiao An ◽  
Tianhong Yang ◽  
Yanpeng Sun
Keyword(s):  
Reconstruction Algorithm ◽  
Spectral Projection ◽  
Radar Imaging ◽  
Basis Pursuit ◽  
Imaging Method ◽  
L1 Norm ◽  
Reconstruction Performance ◽  
Imaging Reconstruction ◽  
Imaging Algorithms

Polarimetric through-the-wall radar imaging (TWRI) system has the enhancing performance in the detection, imaging, and classification of concealed targets behind the wall. We propose a group sparse basis pursuit denoising- (BPDN-) based imaging approach for polarimetric TWRI system in this paper. The proposed imaging method combines the spectral projection gradient L1-norm (SPGL1) algorithm with the nonuniform fast Fourier transform (NUFFT) technique to implement the imaging reconstruction of observed scene. The experimental results have demonstrated that compared to the existing compressive sensing- (CS-) based imaging algorithms, the proposed NUFFT-based SPGL1 algorithm can significantly reduce the required computer memory and achieve the improved imaging reconstruction performance with the high computational efficiency.

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 Imaging Algorithm for Sea-Surface Ship Targets Based on Block Sparsity

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhang Lin ◽  
Jiang Yicheng
Keyword(s):  
High Resolution ◽  
Phased Array ◽  
Matching Pursuit ◽  
Three Dimensional ◽  
Sea Surface ◽  
Sea Waves ◽  
Process Data ◽  
Real Time Processing ◽  
Surface Ship ◽  
Imaging Algorithms

In this study, a phased array radar was used to accurately image stationary and moving ship targets on the vast sea surface. To solve the challenge in real-time processing of the massive amount of data generated by phased array synthetic-aperture radar imaging, this study leveraged the block sparse characteristics of ships on the sea surface and adopted the joint block orthogonal matching pursuit algorithm to obtain high-resolution one-dimensional range images. By only estimating the azimuth Doppler parameters of the targets within the range gates, the amount of process data was significantly reduced, and the data processing speed was enhanced. The synchrosqueezing transform-STFT algorithm was introduced to perform transient imaging as a solution to the blurred imaging of ships due to the three-dimensional swing under the action of waves. The images of the targets were obtained from different squint angles of the antenna array, which improved the imaging accuracy of ships on a vast sea surface. Compared with traditional imaging algorithms, this algorithm can effectively overcome the interference of sea clutter on ship imaging and the influence of sea waves on ship wobble; it can also obtain high-resolution imaging for both stationary and moving targets in a limited amount of time.

A High-resolution Imaging Algorithm for MIMO SAR Based on the Sub-band Synthesis in Frequency Domain

2011 ◽  
Vol 33 (5) ◽  
pp. 1082-1087 ◽  
Author(s):  
Yun-kai Deng ◽  
Qian Chen ◽  
Hai-ming Qi ◽  
Hui-fang Zheng ◽  
Ya-dong Liu
Keyword(s):  
High Resolution ◽  
Frequency Domain ◽  
High Resolution Imaging ◽  
Imaging Algorithm ◽  
Resolution Imaging

scholarly journals A workflow for streamlined acquisition and correlation of serial regions of interest in array tomography

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Sergio Gabarre ◽  
Frank Vernaillen ◽  
Pieter Baatsen ◽  
Katlijn Vints ◽  
Christopher Cawthorne ◽  
...  
Keyword(s):  
High Resolution ◽  
Light And Electron Microscopy ◽  
User Interaction ◽  
Regions Of Interest ◽  
Imaging Method ◽  
Array Tomography ◽  
Reference Space ◽  
Cell Processes ◽  
Electron Microscopes ◽  
Specific Tissue

Abstract Background Array tomography (AT) is a high-resolution imaging method to resolve fine details at the organelle level and has the advantage that it can provide 3D volumes to show the tissue context. AT can be carried out in a correlative way, combing light and electron microscopy (LM, EM) techniques. However, the correlation between modalities can be a challenge and delineating specific regions of interest in consecutive sections can be time-consuming. Integrated light and electron microscopes (iLEMs) offer the possibility to provide well-correlated images and may pose an ideal solution for correlative AT. Here, we report a workflow to automate navigation between regions of interest. Results We use a targeted approach that allows imaging specific tissue features, like organelles, cell processes, and nuclei at different scales to enable fast, directly correlated in situ AT using an integrated light and electron microscope (iLEM-AT). Our workflow is based on the detection of section boundaries on an initial transmitted light acquisition that serves as a reference space to compensate for changes in shape between sections, and we apply a stepwise refinement of localizations as the magnification increases from LM to EM. With minimal user interaction, this enables autonomous and speedy acquisition of regions containing cells and cellular organelles of interest correlated across different magnifications for LM and EM modalities, providing a more efficient way to obtain 3D images. We provide a proof of concept of our approach and the developed software tools using both Golgi neuronal impregnation staining and fluorescently labeled protein condensates in cells. Conclusions Our method facilitates tracing and reconstructing cellular structures over multiple sections, is targeted at high resolution ILEMs, and can be integrated into existing devices, both commercial and custom-built systems.

scholarly journals Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing

2021 ◽  
Vol 11 (4) ◽  
pp. 1435
Author(s):  
Xue Bi ◽  
Lu Leng ◽  
Cheonshik Kim ◽  
Xinwen Liu ◽  
Yajun Du ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Energy Criterion ◽  
Step Size ◽  
Relationship Analysis ◽  
Support Set ◽  
Reconstruction Performance ◽  
Sparsity Level

Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the sparsity level and potentially presents better reconstruction performance than other greedy pursuit algorithms. However, SAMP still suffers from being sensitive to the step size selection at high sub-sampling ratios. To solve this problem, this paper proposes a constrained backtracking matching pursuit (CBMP) algorithm for image reconstruction. The composite strategy, including two kinds of constraints, effectively controls the increment of the estimated sparsity level at different stages and accurately estimates the true support set of images. Based on the relationship analysis between the signal and measurement, an energy criterion is also proposed as a constraint. At the same time, the four-to-one rule is improved as an extra constraint. Comprehensive experimental results demonstrate that the proposed CBMP yields better performance and further stability than other greedy pursuit algorithms for image reconstruction.

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