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

Wang, Yong; Abdelkader, Ali Cherif; Zhao, Bin; Wang, Jinxiang

doi:10.3390/s150922401

Cited by 26 publications

(10 citation statements)

References 39 publications

(42 reference statements)

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“…The fundamental procedure of PSO is as shown in Figure 2. (6) where ′ c ( ) S f is the residual signal in the frequency domain and b f is the neighborhood range. The minimum neighborhood is = b 1/ f T ; the robustness can be increased by extending the neighborhood range properly.…”

Section: Standard Pso Algorithm Applied In Fde-ajtf Decompositionmentioning

confidence: 99%

“…To process mc-PPS effectively, two methods are popularly adopted. The first is polynomial phase transformation (PPT) [5], whether discrete polynomial phase transformation (DPT) [6], high-order ambiguity function (HAF) [7], cubic phase function (CPF) [8], and so forth, all of which can reduce the phase order based on phase differentiation (PD) and reduce the searching space to one dimension. These methods are influenced by the cross terms and their resolutions are limited by the PD process.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

Lao

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.

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Section: Standard Pso Algorithm Applied In Fde-ajtf Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

Lao

et al. 2019

Electronics

View full text Add to dashboard Cite

show abstract

“…A detailed review of QML is presented in reference [13]. The second generally adopted method is polynomial phase transformation (PPT) [14], whether discrete polynomial phase transformation (DPT) [15], high-order ambiguity function (HAF) [16], or cubic phase function (CPF) [17], etc., all of which can reduce the phase order based on phase differentiation (PD) and reduce the searching space to one dimension. These methods are influenced by the cross terms and their resolutions are limited by the PD process.…”

Section: Introductionmentioning

confidence: 99%

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

Lao

Chao

et al. 2018

Electronics

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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.

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“…ISAR imagery plays an important role especially in military applications such as target identification, recognition, and classification [1,2,3,4,5,6]. For ground-based radars, a perspective blind zone will exist when the height of the measured target is too low.…”

Section: Introductionmentioning

confidence: 99%

Non-Stationary Platform Inverse Synthetic Aperture Radar Maneuvering Target Imaging Based on Phase Retrieval

Shi

Xia

Qin

et al. 2018

Sensors

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As a powerful signal processing tool for imaging moving targets, placing radar on a non-stationary platform (such as an aerostat) is a future direction of Inverse Synthetic Aperture Radar (ISAR) systems. However, more phase errors are introduced into the received signal due to the instability of the radar platform, making it difficult for popular algorithms to accurately perform motion compensation, which leads to severe effects in the resultant ISAR images. Moreover, maneuvering targets may have complex motion whose motion parameters are unknown to radar systems. To overcome the issue of non-stationary platform ISAR autofocus imaging, a high-resolution imaging method based on the phase retrieval principle is proposed in this paper. Firstly, based on the spatial geometric and echo models of the ISAR maneuvering target, we can deduce that the radial motion of the radar platform or the vibration does not affect the modulus of the ISAR echo signal, which provides a theoretical basis for the phase recovery theory for the ISAR imaging. Then, we propose an oversampling smoothness (OSS) phase retrieval algorithm with prior information, namely, the phase of the blurred image obtained by the classical imaging algorithm replaces the initial random phase in the original OSS algorithm. In addition, the size of the support domain of the OSS algorithm is set with respect to the blurred target image. Experimental simulation shows that compared with classical imaging methods, the proposed method can obtain the resultant motion-compensated ISAR image without estimating the radar platform and maneuvering target motion parameters, wherein the fictitious target is perfectly focused.

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

Cited by 26 publications

References 39 publications

A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

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

Non-Stationary Platform Inverse Synthetic Aperture Radar Maneuvering Target Imaging Based on Phase Retrieval

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