scholarly journals Fast Split Bregman Based Deconvolution Algorithm for Airborne Radar Imaging

2020 ◽  
Vol 12 (11) ◽  
pp. 1747 ◽  
Author(s):  
Yin Zhang ◽  
Qiping Zhang ◽  
Yongchao Zhang ◽  
Jifang Pei ◽  
Yulin Huang ◽  
...  
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Real Data ◽  
Radar Imaging ◽  
Matrix Inversion ◽  
Airborne Radar ◽  
Split Bregman ◽  
Regularization Problem ◽  
Azimuth Resolution ◽  
Split Bregman Algorithm

Deconvolution methods can be used to improve the azimuth resolution in airborne radar imaging. Due to the sparsity of targets in airborne radar imaging, an L 1 regularization problem usually needs to be solved. Recently, the Split Bregman algorithm (SBA) has been widely used to solve L 1 regularization problems. However, due to the high computational complexity of matrix inversion, the efficiency of the traditional SBA is low, which seriously restricts its real-time performance in airborne radar imaging. To overcome this disadvantage, a fast split Bregman algorithm (FSBA) is proposed in this paper to achieve real-time imaging with an airborne radar. Firstly, under the regularization framework, the problem of azimuth resolution improvement can be converted into an L 1 regularization problem. Then, the L 1 regularization problem can be solved with the proposed FSBA. By utilizing the low displacement rank features of Toeplitz matrix, the proposed FSBA is able to realize fast matrix inversion by using a Gohberg–Semencul (GS) representation. Through simulated and real data processing experiments, we prove that the proposed FSBA significantly improves the resolution, compared with the Wiener filtering (WF), truncated singular value decomposition (TSVD), Tikhonov regularization (REGU), Richardson–Lucy (RL), iterative adaptive approach (IAA) algorithms. The computational advantage of FSBA increases with the increase of echo dimension. Its computational efficiency is 51 times and 77 times of the traditional SBA, respectively, for echoes with dimensions of 218 × 400 and 400 × 400 , optimizing both the image quality and computing time. In addition, for a specific hardware platform, the proposed FSBA can process echo of greater dimensions than traditional SBA. Furthermore, the proposed FSBA causes little performance degradation, when compared with the traditional SBA.

scholarly journals Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method

2021 ◽  
Vol 13 (4) ◽  
pp. 549
Author(s):  
Qiping Zhang ◽  
Yin Zhang ◽  
Yongchao Zhang ◽  
Yulin Huang ◽  
Jianyu Yang
Keyword(s):  
Computational Complexity ◽  
Total Variation ◽  
Super Resolution ◽  
Real Data ◽  
Radar Imaging ◽  
Matrix Inversion ◽  
Variation Method ◽  
Airborne Radar ◽  
The Matrix ◽  
Very High

Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform.

scholarly journals A Superfast Super-Resolution Method for Radar Forward-Looking Imaging

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 817
Author(s):  
Weibo Huo ◽  
Qiping Zhang ◽  
Yin Zhang ◽  
Yongchao Zhang ◽  
Yulin Huang ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Super Resolution ◽  
Coefficient Matrix ◽  
Resolution Method ◽  
The Real ◽  
L1 Regularization ◽  
Regularization Problem ◽  
Azimuth Resolution ◽  
Forward Looking

The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an L1 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable L1 regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg-Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from O(N3) to O(N2). It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice.

Multiple Aerosol Unmixing by the Split Bregman Algorithm

2011 ◽  
Author(s):  
Russell Warren ◽  
Stanley Osher ◽  
Richard Vanderbeek
Keyword(s):  
Split Bregman ◽  
Split Bregman Algorithm

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

Towards real-time fire data synthesis using numerical simulations

2021 ◽  
pp. 073490412199344
Author(s):  
Wolfram Jahn ◽  
Frane Sazunic ◽  
Carlos Sing-Long
Keyword(s):  
Real Time ◽  
Computational Fluid Dynamic ◽  
Dynamic Models ◽  
Fluid Dynamic ◽  
Near Field ◽  
Computing Time ◽  
Temperature Correction ◽  
Dynamic Simulations ◽  
Conservation Of Energy ◽  
Fire Scenarios

Synthesising data from fire scenarios using fire simulations requires iterative running of these simulations. For real-time synthesising, faster-than-real-time simulations are thus necessary. In this article, different model types are assessed according to their complexity to determine the trade-off between the accuracy of the output and the required computing time. A threshold grid size for real-time computational fluid dynamic simulations is identified, and the implications of simplifying existing field fire models by turning off sub-models are assessed. In addition, a temperature correction for two zone models based on the conservation of energy of the hot layer is introduced, to account for spatial variations of temperature in the near field of the fire. The main conclusions are that real-time fire simulations with spatial resolution are possible and that it is not necessary to solve all fine-scale physics to reproduce temperature measurements accurately. There remains, however, a gap in performance between computational fluid dynamic models and zone models that must be explored to achieve faster-than-real-time fire simulations.

scholarly journals Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings

2021 ◽  
Vol 13 (5) ◽  
pp. 2426
Author(s):  
David Bienvenido-Huertas ◽  
Jesús A. Pulido-Arcas ◽  
Carlos Rubio-Bellido ◽  
Alexis Pérez-Fargallo
Keyword(s):  
Low Income ◽  
Potential Risk ◽  
Energy Use ◽  
Risk Index ◽  
Computing Time ◽  
Simulated Data ◽  
Real Data ◽  
Support Vector ◽  
Energy Poverty ◽  
Regression Algorithms

In recent times, studies about the accuracy of algorithms to predict different aspects of energy use in the building sector have flourished, being energy poverty one of the issues that has received considerable critical attention. Previous studies in this field have characterized it using different indicators, but they have failed to develop instruments to predict the risk of low-income households falling into energy poverty. This research explores the way in which six regression algorithms can accurately forecast the risk of energy poverty by means of the fuel poverty potential risk index. Using data from the national survey of socioeconomic conditions of Chilean households and generating data for different typologies of social dwellings (e.g., form ratio or roof surface area), this study simulated 38,880 cases and compared the accuracy of six algorithms. Multilayer perceptron, M5P and support vector regression delivered the best accuracy, with correlation coefficients over 99.5%. In terms of computing time, M5P outperforms the rest. Although these results suggest that energy poverty can be accurately predicted using simulated data, it remains necessary to test the algorithms against real data. These results can be useful in devising policies to tackle energy poverty in advance.

scholarly journals Sparse Functional Linear Discriminant Analysis

Biometrika ◽  
2021 ◽  
Author(s):  
Juhyun Park ◽  
Jeongyoun Ahn ◽  
Yongho Jeon
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Real Data ◽  
New Approach ◽  
Linear Discriminant ◽  
Finite Samples ◽  
Regularization Problem ◽  
Bayes Error ◽  
Functional Setting ◽  
Regularized Solution

Abstract Functional linear discriminant analysis offers a simple yet efficient method for classification, with the possibility of achieving a perfect classification. Several methods are proposed in the literature that mostly address the dimensionality of the problem. On the other hand, there is a growing interest in interpretability of the analysis, which favors a simple and sparse solution. In this work, we propose a new approach that incorporates a type of sparsity that identifies nonzero sub-domains in the functional setting, offering a solution that is easier to interpret without compromising performance. With the need to embed additional constraints in the solution, we reformulate the functional linear discriminant analysis as a regularization problem with an appropriate penalty. Inspired by the success of ℓ1-type regularization at inducing zero coefficients for scalar variables, we develop a new regularization method for functional linear discriminant analysis that incorporates an L1-type penalty, ∫ |f|, to induce zero regions. We demonstrate that our formulation has a well-defined solution that contains zero regions, achieving a functional sparsity in the sense of domain selection. In addition, the misclassification probability of the regularized solution is shown to converge to the Bayes error if the data are Gaussian. Our method does not presume that the underlying function has zero regions in the domain, but produces a sparse estimator that consistently estimates the true function whether or not the latter is sparse. Numerical comparisons with existing methods demonstrate this property in finite samples with both simulated and real data examples.

scholarly journals Real Time Simulation of Wind Farm Connected MMC-HVDC System

2020 ◽  
Vol 8 (6S) ◽  
pp. 46-52
Keyword(s):  
Real Time ◽  
Wind Farm ◽  
Computing Time ◽  
Offshore Wind ◽  
Modular Multilevel Converter ◽  
Offshore Wind Farm ◽  
Multilevel Converter ◽  
Real Time Simulation ◽  
Hvdc System ◽  
Time Simulation

Real time simulators play a major role in R&D of Offshore wind farm connected modular multilevel converter (MMC)-HVDC system. These simulators are used for testing the actual prototype of controllers or protection equipment required for the system under study. Modular multilevel converter comprises of number of sub modules (SMs) like Half/ full bridge cells. While computing time domain Electromagnetic transients (EMTs) with the system having large number of SMs pose a great challenge. This computational burden will be more when simulated in real time. To overcome this, several authors proposed equivalent mathematical model of MMC. This paper proposes the real time simulation start-up of offshore wind farm connected modular multilevel converter (MMC)-HVDC system. This paper also describes about how the above said systems is simulated in OPAL-RT based Hypersim software.

Sign in / Sign up

Export Citation Format

Share Document