Cubic phase function: A simple solution to polynomial phase signal analysis

Djurovi, Igor; Simeunovi, Marko; Pu, Wei

doi:10.1016/j.sigpro.2016.12.027

Cited by 63 publications

(26 citation statements)

References 220 publications

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“…Known from equation 22, γK 2 11 ≈ γ/c 2 ≪ 1, so the phase term exp jπγK 2 11 t 4 m can be negligible, and therefore, the echo can be regarded as a cubic phase function with respect to t m [21]. The cubic phase function (CPF) [22] can be used to estimate the third-order phase coefficient η 1 and the second-order coefficient η 2 . With them, the phase compensation function can be constructed as S cmp t m = exp jπ η 1 t 3 m + η 2 t 2 m , and after Doppler compensation, the echo is…”

Section: B-isar Range-doppler Imaging Analysis Ofmentioning

confidence: 99%

“…The difference between the method proposed in Section 4 and the method proposed in [12] is the introduction of speed compensation and Doppler compensation, and the cost time of these two parts can be calculated according to [20][21][22]. However, the computation complexity of the sparse imaging step is the same as the method in [12].…”

Section: Verification Of the Parameter Estimation Accuracymentioning

confidence: 99%

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Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing

Zeng

Zhu

Xin

2019

International Journal of Antennas and Propagation

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Bistatic inverse synthetic aperture radar (ISAR) can increase the probability of tracking the high-speed target and provide more angle information than monostatic ISAR. However, bistatic ISAR suffers from a serious defocusing problem, resulting from the high-speed motion. Furthermore, the inherent geometry distortion and calibration problems make bistatic ISAR (B-ISAR) imaging more complex. In response to these problems, we propose a bistatic ISAR imaging method for high-speed moving target with geometric distortion correction and calibration based on dechirping processing. At first, based on the motion decomposition idea, the B-ISAR echo model of the high-speed moving target is established. Then, by analyzing the imaging mechanism of the Range-Doppler algorithm (RDA), we eliminate the phase items influencing the imaging quality with speed compensation and Doppler compensation. After that, the analytic formula of the geometric distortion factor and calibration factor are deduced, which helps transform the geometric correction and calibration problem into a parameter estimation problem. Finally, with the sparsity of the scattering points, the required parameters are solved using the expectation maximization (EM) algorithm based on the maximum a posteriori probability criterion. With the estimated parameters, a clear B-ISAR image of a high-speed moving target with geometric correction and calibration is obtained. The simulations show that the proposed method has a better resolution and simultaneously attains geometric correction and calibration of the image.

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Section: B-isar Range-doppler Imaging Analysis Ofmentioning

confidence: 99%

Section: Verification Of the Parameter Estimation Accuracymentioning

confidence: 99%

Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing

Zeng

Zhu

Xin

2019

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

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“…Although many modified methods have been proposed to reduce the cross terms, such as the product forms of HAF (PAHF) [9] and CPF (PCPF) [10], these methods are also influenced by the cross terms of the mc-PPS, especially when numerous components are contained and the intensities of every component are similar. The PPT methods are reviewed in detail in [11].…”

Section: Introductionmentioning

confidence: 99%

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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“…On the one hand, 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 reduces relatively. On the other hand, more complex changes in target gesture lead to higher orders in the echo phase and inconsistant methods can reduce the phase order by certain processes and reduce the search space to one dimension, which is reviewed in detail in the cited paper [17]. The DPT and classical HAF methods allow each order parameter of a PPS to be obtained by searching the maximum after Fourier transform of its PD.…”

Section: Introductionmentioning

confidence: 99%

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

Lao

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.

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Cubic phase function: A simple solution to polynomial phase signal analysis

Cited by 63 publications

References 220 publications

Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing

Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing

A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

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

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