The Modeling and Simulation of Spaceborne SAR Moving Vessel Imaging Based on Matlab Class

Chao

et al. 2018

Electronics

Self Cite

In high-resolution spaceborne synthetic aperture radar (SAR), imaging of moving ship targets is strongly influenced by ships' complex three-axis motions, so that imaging results are fuzzy and unfocused. Yet scattered and moving information on ship targets is wholly contained in the complex image data. This paper proposes a novel SAR and inverse SAR (SAR-ISAR) hybrid imaging method to improve imaging effects, using this complex SAR image data on ship targets, and based on frequency-domain-extraction-based adaptive joint time frequency (FDE-AJTF) decomposition. First, complex SAR image data is transformed to the Doppler domain in the azimuth dimension, and the optimum azimuth data are selected. Next, the signal in each range cell is decomposed to its polynomial phase signal (PPS) components by FDE-AJTF. Finally, a two-dimensional image of the ship target at a given azimuth time is constructed directly. The feasibility and effectiveness of this proposed imaging method is verified through comparisons with conventional methods in simulation and experimental tests.

Section: Simulation Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An SAR-ISAR Hybrid Imaging Method for Ship Targets Based on FDE-AJTF Decomposition

Chao

et al. 2018

Electronics

Self Cite

“…Synthetic aperture radar (SAR) and inverse SAR (ISAR), which have all-time and all-weather active imaging abilities, play important roles in the civil and military fields, and their echo signals' processing has always been a research focus and hotspot. The echo of maneuvering targets, such as ships, aircraft, and space debris, can be expressed as a multicomponent polynomial phase signal (mc-PPS) [1][2][3]. Especially with the improvement of radar resolution and increases of the synthetic period, there arise new influences from two aspects: First, the number of signal components is increased with more resolvable scattering elements, while the component extraction is more difficult and easily interfered with by noises as the energy of each single component is reduced relatively.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

et al. 2019

Electronics

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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.

“…Synthetic aperture radar (SAR) and inverse SAR (ISAR), which have all-time and all-weather active imaging abilities, play important roles in the civil and military fields, and the echo signal processing of SAR and ISAR has always been a research focus and hotspot. SAR and ISAR images on ships, aircraft, space debris, and other maneuvering targets, however, are seriously influenced by the high-order Doppler in the radar echo phases caused by their complex motions [1][2][3][4]. The maneuvering target radar echo can be expressed as a multicomponent polynomial phase signal (mc-PPS), which should be parameterized by the time frequency tool in high-precision processing [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Frequency Domain Extraction Based Adaptive Joint Time Frequency Decomposition Method of the Maneuvering Target Radar Echo

Chao

et al. 2018

Remote Sensing

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Abstract:The maneuvering target echo of high-resolution radar can be expressed as a multicomponent polynomial phase signal (mc-PPS). However, with improvements in radar resolution and increases in the synthetic period, classical time frequency analysis methods cannot satisfy the requirements of maneuvering target radar echo processing. In this paper, a novel frequency domain extraction-based adaptive joint time frequency (FDE-AJTF) decomposition method was proposed with three improvements. First, the maximum frequency spectrum of the phase compensation signal was taken as the fitness function, while the fitness comparison, component extraction, and residual updating were operated in the frequency domain; second, the time window was adopted on the basis function to fit the uncertain signal component time; and third, constant false alarm ratio (CFAR) detection was applied in the component extraction to reduce the ineffective components. Through these means, the stability and speed of phase parameters estimation increased with one domination ignored in the phase parameter estimation, and the accuracy and effectiveness of the signal component extraction performed better with less influence from the estimation errors, clutters, and noises. Finally, these advantages of the FDE-AJTF decomposition method were verified through a comparison with the classical method in simulation and experimental tests.