scholarly journals ISAR Imaging of Maneuvering Targets Based on the Modified Discrete Polynomial-Phase Transform

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 22401-22418 ◽  
Author(s):  
Yong Wang ◽  
Ali Abdelkader ◽  
Bin Zhao ◽  
Jinxiang Wang
Keyword(s):  
Maneuvering Targets ◽  
Polynomial Phase ◽  
Phase Transform ◽  
Isar Imaging

A Novel ISAR Imaging Approach for Maneuvering Targets With Satellite-Borne Platform

2021 ◽  
pp. 1-5
Author(s):  
Dong Li ◽  
Jinzhi Ren ◽  
Hongqing Liu ◽  
Zhijun Yang ◽  
Jun Wan ◽  
...  
Keyword(s):  
Maneuvering Targets ◽  
Isar Imaging ◽  
Imaging Approach

scholarly journals A NOVEL METHOD FOR ISAR IMAGING OF MULTIPLE MANEUVERING TARGETS

2019 ◽  
Vol 81 ◽  
pp. 43-54 ◽  
Author(s):  
Jia Zhao ◽  
Yun-Qi Zhang ◽  
Xin Wang ◽  
Sheng Wang ◽  
Feng Shang
Keyword(s):  
Maneuvering Targets ◽  
Isar Imaging ◽  
Novel Method

scholarly journals A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 51
Author(s):  
Lei Yu ◽  
Guochao Lao ◽  
Chunsheng Li ◽  
Yang Sun ◽  
Yingying Li
Keyword(s):  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Optimal Parameters ◽  
Time Frequency ◽  
Modified Maximum Likelihood ◽  
Maneuvering Targets ◽  
Polynomial Phase ◽  
Phase Signal ◽  
Evolution Algorithm

The echo of maneuvering targets can be expressed as a multicomponent polynomial phase signal (mc-PPS), which should be processed by time frequency analysis methods, while, as a modified maximum likelihood (ML) method, the frequency domain extraction-based adaptive joint time frequency (FDE–AJTF) decomposition method is an effective tool. However, the key procedure in the FDE–AJTF method is searching for the optimal parameters in the solution space, which is essentially a multidimensional optimization problem with different extremal solutions. To solve the problem, a novel multicomponent particle swarm optimization (mc-PSO) algorithm is presented and applied in the FDE–AJTF decomposition with the new characteristic that can extract several components simultaneously based on the feature of the standard PSO, in which the population is divided into three groups and the neighborhood of the best particle in the optimal group is set as the forbidden area for the suboptimal group, and then two different independent components can be obtained and extracted in one extraction. To analyze its performance, three simulation tests are carried out and compared with a standard PSO, genetic algorithm, and differential evolution algorithm. According to the tests, it is verified that the mc-PSO has the best performance in that the convergence, accuracy, and stability are improved, while its searching times and computation are reduced.

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