scholarly journals Code Design for Moving Target-Detecting Radar in Nonhomogeneous Signal-Dependent Clutter

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jindong Zhang ◽  
Yin Du ◽  
He Yan
Keyword(s):  
Optimization Problem ◽  
Matched Filtering ◽  
Code Design ◽  
Moving Target ◽  
Computationally Efficient ◽  
Numerical Examples ◽  
Radar Systems ◽  
Minimization Algorithms ◽  
Simplified Algorithm ◽  
Target Detecting

In this paper, we investigate the code design problem of improving the detection performance of a moving target in the presence of nonhomogeneous signal-dependent clutter for moving target-detecting (MTD) radar systems. The optimization metric is constructed based on the signal to clutter and noise ratio (SCNR) of interpulse matched filtering. Under the frameworks of cyclic and majorization-minimization algorithms, we propose a novel algorithm, named CMMCODE, to tackle the code design optimization problem in the case of unknown precise target Doppler information and nonhomogeneous clutter. In the white-noise case, the simplified algorithm is also given based on CMMCODE algorithm. The presented algorithm is computationally efficient and convergent. Numerical examples show the effectiveness of the proposed algorithms.

scholarly journals Ground Moving Target Imaging via SDAP-ISAR Processing: Review and New Trends

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2391
Author(s):  
Marco Martorella ◽  
Samuele Gelli ◽  
Alessio Bacci
Keyword(s):  
Homeland Security ◽  
Moving Target ◽  
Security Applications ◽  
Ground Clutter ◽  
Radar Systems ◽  
Joint Application ◽  
Target Imaging ◽  
Moving Target Imaging ◽  
Space Time Adaptive Processing ◽  
Border Surveillance

Ground moving target imaging finds its main applications in both military and homeland security applications, with examples in operations of intelligence, surveillance and reconnaissance (ISR) as well as border surveillance. When such an operation is performed from the air looking down towards the ground, the clutter return may be comparable or even stronger than the target’s, making the latter hard to be detected and imaged. In order to solve this problem, multichannel radar systems are used that are able to remove the ground clutter and effectively detect and image moving targets. In this feature paper, the latest findings in the area of Ground Moving Target Imaging are revisited that see the joint application of Space-Time Adaptive Processing and Inverse Synthetic Aperture Radar Imaging. The theoretical aspects analysed in this paper are supported by practical evidence and followed by application-oriented discussions.

The Equilibrium Cell-Based Smooth Finite Element Method for Shakedown Analysis of Structures

2019 ◽  
Vol 16 (05) ◽  
pp. 1840013 ◽  
Author(s):  
P. L. H. Ho ◽  
C. V. Le ◽  
T. Q. Chu
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Optimization Problem ◽  
Conic Programming ◽  
Equilibrium Equations ◽  
Shakedown Analysis ◽  
Numerical Examples ◽  
Elastic Stresses ◽  
Gauss Points ◽  
Element Method

This paper presents a novel equilibrium formulation, that uses the cell-based smoothed method and conic programming, for limit and shakedown analysis of structures. The virtual strains are computed using straining cell-based smoothing technique based on elements of discretized mesh. Fictitious elastic stresses are also determined within the framework of finite element method (CS-FEM)-based Galerkin procedure, and equilibrium equations for residual stresses are satisfied in an average sense at every cell-based smoothing cell. All constrains are imposed at only one point in the smoothing domains, instead of Gauss points as in a standard FEM-based procedure. The resulting optimization problem is then handled using the highly efficient solvers. Various numerical examples are investigated, and obtained solutions are compared with available results in the literature.

scholarly journals Between hard and soft thresholding: optimal iterative thresholding algorithms

2019 ◽  
Vol 9 (4) ◽  
pp. 899-933 ◽  
Author(s):  
Haoyang Liu ◽  
Rina Foygel Barber
Keyword(s):  
General Class ◽  
Optimization Problem ◽  
Computationally Efficient ◽  
Hard Thresholding ◽  
Worst Case ◽  
Soft Thresholding ◽  
Iterative Thresholding ◽  
New Class ◽  
Rank Constraint ◽  
Convergence Performance

Abstract Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator and asks whether it is possible to achieve stronger guarantees than what is possible with hard thresholding. We develop the notion of relative concavity of a thresholding operator, a quantity that characterizes the worst-case convergence performance of any thresholding operator on the target optimization problem. Surprisingly, we find that commonly used thresholding operators, such as hard thresholding and soft thresholding, are suboptimal in terms of worst-case convergence guarantees. Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators. Examples of this general class includes $\ell _q$ thresholding with appropriate choices of $q$ and a newly defined reciprocal thresholding operator. We also investigate the implications of the improved optimization guarantee in the statistical setting of sparse linear regression and show that this new class of thresholding operators attain the optimal rate for computationally efficient estimators, matching the Lasso.

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