scholarly journals Fast off grid compressed sensing ISAR imaging algorithm

2018 ◽  
Vol 69 (4) ◽  
pp. 326-328
Author(s):  
Cheng Ping ◽  
Zhao Jiaqun
Keyword(s):  
Compressed Sensing ◽  
Optimization Problem ◽  
Synthetic Aperture ◽  
Joint Optimization ◽  
Least Squares Problems ◽  
Imaging Algorithm ◽  
First Order ◽  
Taylor Approximation ◽  
Isar Imaging ◽  
Conventional Algorithm

Abstract To solve the off grid problem in compressed sensing (CS) based inverse synthetic aperture radar (ISAR) imaging, a fast and accurate algorithm has been proposed in the paper. By jointly estimating the off grid error and the sparse solution, off grid ISAR imaging is transformed into a joint optimization problem. Interestingly, it can be solved efficiently through two least squares problems based on first order Taylor approximation. When applied to complex sinusoids and quasi real ISAR data, the proposed algorithm has got better results than the conventional algorithm. Therefore, it is a promising off grid CS based ISAR imaging algorithm.

scholarly journals New Compressed Sensing ISAR Imaging Algorithm Based on Log–Sum Minimization

2016 ◽  
Vol 67 (6) ◽  
pp. 439-443
Author(s):  
Cheng Ping ◽  
Zhao Jiaqun
Keyword(s):  
Compressed Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Signal Spectrum ◽  
Imaging Algorithm ◽  
Imaging Result ◽  
Isar Imaging ◽  
Recovery Error ◽  
New Interpretation ◽  
Conventional Algorithm

Abstract To improve the performance of inverse synthetic aperture radar (ISAR) imaging based on compressed sensing (CS), a new algorithm based on log-sum minimization is proposed. A new interpretation of the algorithm is also provided. Compared with the conventional algorithm, the new algorithm can recover signals based on fewer measurements, in looser sparsity condition, with smaller recovery error, and it has obtained better sinusoidal signal spectrum and imaging result for real ISAR data. Therefore, the proposed algorithm is a promising imaging algorithm in CS ISAR.

scholarly journals Bistatic ISAR Imaging with a V-FM Waveform Based on a Dual-Channel-Coupled 2D-CS Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3082 ◽  
Author(s):  
Jiyuan Chen ◽  
Xiaoyi Pan ◽  
Letao Xu ◽  
Wei Wang
Keyword(s):  
Compressed Sensing ◽  
Optimization Problem ◽  
Super Resolution ◽  
Space Distribution ◽  
Signal Model ◽  
Low Snr ◽  
Dual Channel ◽  
Isar Imaging ◽  
Radar Echo ◽  
Resolution Model

Due to the sparsity of the space distribution of point scatterers and radar echo data, the theory of Compressed Sensing (CS) has been successfully applied in Inverse Synthetic Aperture Radar (ISAR) imaging, which can recover an unknown sparse signal from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, since the V style modulation(V-FM) signal can mitigate the ambiguity apparent in range and velocity, the dual-channel, two-dimension, compressed-sensing (2D-CS) algorithm is proposed for Bistatic ISAR (Bi-ISAR) imaging, which directly deals with the 2D signal model for image reconstruction based on solving a nonconvex optimization problem. The coupled 2D super-resolution model of the target’s echoes is firstly established; then, the 2D-SL0 algorithm is applied in each channel with different dictionaries, and the final image is obtained by synthesizing the two channels. Experiments are used to test the robustness of the Bi-ISAR imaging framework with the two-dimensional CS method. The results show that the framework is capable accurately reconstructing the Bi-ISAR image within the conditions of low SNR and low measured data.

scholarly journals An Improved Space Debris ISAR Imaging Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 321-326
Author(s):  
Bao Ping Wang ◽  
Jun Jie Guo ◽  
Chao Sun
Keyword(s):  
Fourier Transform ◽  
Synthetic Aperture Radar ◽  
Space Debris ◽  
Position Error ◽  
Synthetic Aperture ◽  
Inverse Synthetic Aperture Radar ◽  
Imaging Algorithm ◽  
Scattering Centers ◽  
Isar Imaging ◽  
Aperture Radar

According to the Characteristics of space debris, single-range matching filtering (SRMF) can be used for space debris inverse synthetic aperture radar (ISAR) imaging. Combined SRMF and Coherent CLEAN algorithm can effectively solve high sidelobes problem brought by Fourier transform. However, when the SNR is low, the position error of scattering centers extracted by SRMF-CLEAN is big, and even some weak scatterings can’t be extracted. This paper uses Sequence CLEAN instead of Coherent CLEAN to availably solve the above problems. The experiment results confirm the validness of the proposed algorithm.

scholarly journals Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Serena Wang ◽  
Maya Gupta ◽  
Seungil You
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Combinatorial Optimization Problem ◽  
Joint Optimization ◽  
Early Stopping ◽  
Time Approximation ◽  
Approximation Bound ◽  
Evaluation Time ◽  
Speed Up ◽  
Real World Problems

Given a classifier ensemble and a dataset, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble is evaluated. Dynamically deciding to classify early can reduce both mean latency and CPU without harming the accuracy of the original ensemble. To achieve such gains, we propose jointly optimizing the evaluation order of the base models and early-stopping thresholds. Our proposed objective is a combinatorial optimization problem, but we provide a greedy algorithm that achieves a 4-approximation of the optimal solution under certain assumptions, which is also the best achievable polynomial-time approximation bound. Experiments on benchmark and real-world problems show that the proposed Quit When You Can (QWYC) algorithm can speed up average evaluation time by 1.8–2.7 times on even jointly trained ensembles, which are more difficult to speed up than independently or sequentially trained ensembles. QWYC’s joint optimization of ordering and thresholds also performed better in experiments than previous fixed orderings, including gradient boosted trees’ ordering.

scholarly journals Joint optimization of computing ratio and access points’ density for mixed mobile edge/cloud computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Tianqi Jing ◽  
Shiwen He ◽  
Fei Yu ◽  
Yongming Huang ◽  
Luxi Yang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Coverage Probability ◽  
Optimization Problem ◽  
Mobile Cloud Computing ◽  
Low Complexity ◽  
Mixed Integer ◽  
Joint Optimization ◽  
Mobile Cloud ◽  
Convex Problem

AbstractCooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point’s (AP) and the cloud, and the density allocation of APs’ at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs’ density allocation contribute to the charge saving.

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