scholarly journals Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bo Fan ◽  
Xiaoli Zhou ◽  
Shuo Chen ◽  
Zhijie Jiang ◽  
Yongqiang Cheng
Keyword(s):  
Model Errors ◽  
Target Image ◽  
Reconstruction Accuracy ◽  
Unified Framework ◽  
Imaging Model ◽  
Error Calibration ◽  
Coincidence Imaging ◽  
Bayesian Prior ◽  
Accurate Imaging ◽  
Reconstruction Model

Sparsity-driven methods are commonly applied to reconstruct targets in radar coincidence imaging (RCI), where the reference matrix needs to be computed precisely and the prior knowledge of the accurate imaging model is essential. Unfortunately, the existence of model errors in practical RCI applications is common, which defocuses the reconstructed image considerably. Accordingly, this paper aims to formulate a unified framework for sparsity-driven RCI with model errors based on the sparse Bayesian approach. Firstly, a parametric joint sparse reconstruction model is built to describe the RCI when perturbed by model errors. The structured sparse Bayesian prior is then assigned to this model, after which the structured sparse Bayesian autofocus (SSBA) algorithm is proposed in the variational Bayesian expectation maximization (VBEM) framework; this solution jointly realizes sparse imaging and model error calibration. Simulation results demonstrate that the proposed algorithm can both calibrate the model errors and obtain a well-focused target image with high reconstruction accuracy.

scholarly journals Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Dong Zhang ◽  
Yongshun Zhang ◽  
Cunqian Feng
Keyword(s):  
Computational Efficiency ◽  
Taylor Expansion ◽  
Sparse Matrix ◽  
Doa Estimation ◽  
Reconstruction Problem ◽  
Reconstruction Accuracy ◽  
Mismatch Problem ◽  
Matrix Reconstruction ◽  
Simulation Results ◽  
Reconstruction Model

Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

scholarly journals Reconstruction of 3D Temperature Profile of Radiative Participatory Flame Based on Digital Refocusing Technique of Light Field Camera

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Biao Zhang ◽  
Chen Wang ◽  
Yudong Liu ◽  
Chuanlong Xu ◽  
Qi Qi
Keyword(s):  
Temperature Profile ◽  
Light Field ◽  
Imaging System ◽  
Flame Temperature ◽  
Reconstruction Accuracy ◽  
Camera System ◽  
Pollutant Formation ◽  
Imaging Model ◽  
Spread Function ◽  
Candle Flame

Accurate and reliable measurements of the 3D flame temperature profile are highly desirable to achieve in-depth understanding of the combustion and pollutant formation processes. In this paper, a measurement method for reconstruction of a 3D flame temperature profile was proposed by using a light field camera. It combines the convolution imaging model and radiative transfer equation and takes into account the characteristics of emission, absorption, and scattering of a semitransparent flame. According to the point spread function characteristics of the imaging system, the number and positions of the refocus planes were set by comprehensive consideration of the reconstruction accuracy and efficiency. The feasibility of the present method was proved by numerical simulation and an experiment of a candle flame. This method achieves the reconstruction of a 3D asymmetric flame profile through a single exposure of a single camera, which overcomes the problem of complexity of a multicamera system and the time delay of a conventional scanning camera system.

Research on Jamming Method of False Target Based on Micro-Motion Modulation

2014 ◽  
Vol 556-562 ◽  
pp. 2707-2710
Author(s):  
Rui Cui ◽  
Ai Guo Sheng ◽  
Ji Fei Pan ◽  
Bing He ◽  
Jing Zhu
Keyword(s):  
Unique Feature ◽  
Target Recognition ◽  
Doppler Imaging ◽  
Radar Target ◽  
Target Image ◽  
Imaging Model ◽  
False Target ◽  
Micro Motion ◽  
Rotating Target

Micro-Doppler is a unique feature of radar target, and has been applied to target recognition of ISAR widely, but it can also destroy the quality of the target image at the same time. So a novel jamming method of false target base on Micro-Doppler modulation is presented in the paper. The phase of captured radar transmitting signal is been modulated, which can generate false Micro-Doppler features. The micro-Doppler imaging model of the rotating target is analyzed, and the jamming model based on Micro-Motion modulation is given. Finally, the simulation of jamming experiment is carried out. The results of simulation prove the method is corrective and effective.

scholarly journals Active Object Reconstruction Using a Guided View Planner

2018 ◽  
Author(s):  
Xin Yang ◽  
Yuanbo Wang ◽  
Yaru Wang ◽  
Baocai Yin ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Information Acquisition ◽  
3D Model ◽  
Alternative Methods ◽  
Active Object ◽  
Reconstruction Accuracy ◽  
Object Reconstruction ◽  
View Planning ◽  
Aggregate Information ◽  
Active Reconstruction ◽  
Reconstruction Model

Inspired by the recent advance of image-based object reconstruction using deep learning, we present an active reconstruction model using a guided view planner. We aim to reconstruct a 3D model using images observed from a planned sequence of informative and discriminative views. But where are such informative and discriminative views around an object? To address this we propose a unified model for view planning and object reconstruction, which is utilized to learn a guided information acquisition model and to aggregate information from a sequence of images for reconstruction. Experiments show that our model (1) increases our reconstruction accuracy with an increasing number of views (2) and generally predicts a more informative sequence of views for object reconstruction compared to other alternative methods.

Joint Neighboring Coding with a Low-Rank Constraint for Multi-Atlas Based Image Segmentation

2020 ◽  
Vol 10 (2) ◽  
pp. 310-315
Author(s):  
Hancan Zhu ◽  
Guanghua He
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Low Rank ◽  
Target Image ◽  
Unified Framework ◽  
Label Fusion ◽  
Rank Constraint ◽  
Robust Segmentation ◽  
Fusion Methods ◽  
Atlas Segmentation

Multi-atlas methods have been successful for solving many medical image segmentation problems. Under the multi-atlas segmentation framework, labels of atlases are first propagated to the target image space with the deformation fields generated by registering atlas images onto a target image, and then these labels are fused to obtain the final segmentation. While many label fusion strategies have been developed, weighting based label fusion methods have attracted considerable attention. In this paper, we first present a unified framework for weighting based label fusion methods. Under this unified framework, we find that most of recent developed weighting based label fusion methods jointly consider the pair-wise dependency between atlases. However, they independently label voxels to be segmented, ignoring their neighboring spatial structure that might be informative for obtaining robust segmentation results for noisy images. Taking into consideration of potential correlation among neighboring voxels to be segmented, we propose a joint coding method (JCM) with a low-rank constraint for the multi-atlas based image segmentation in a general framework that unifies existing weighting based label fusion methods. The method has been validated for segmenting hippocampus from MR images. It is demonstrated that our method can achieve competitive segmentation performance as the state-of-the-art methods, especially when the quality of images is poor.

A new reconstruction algorithm based on temporal neural network and its application in power quality disturbance data

2021 ◽  
pp. 002029402110197
Author(s):  
Yan Liu ◽  
Wei Tang ◽  
Yiduo Luan
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Large Scale ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Reconstruction Accuracy ◽  
Power Quality Disturbance ◽  
Fully Connected ◽  
Reconstruction Model ◽  
Reconstruction Time

The traditional reconstruction algorithms based on p-norm, limited by their reconstruction model and data processing mode, are prone to reconstruction failure or long reconstruction time. In order to break through the limitations, this paper proposes a reconstruction algorithm based on the temporal neural network (TCN). A new reconstruction model based on TCN is first established, which does not need sparse representation and has large-scale parallel processing. Next, a TCN with a fully connected layer and symmetrical zero-padding operation is designed to meet the reconstruction requirements, including non-causality and length-inconsistency. Moreover, the proposed algorithm is constructed and applied to power quality disturbance (PQD) data. Experimental results show that the proposed algorithm can implement the reconstruction task, demonstrating better reconstruction accuracy and less reconstruction time than OMP, ROMP, CoSaMP, and SP. Therefore, the proposed algorithm is more attractive when dictionary design is complicated, or real-time reconstruction is required.

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