scholarly journals A Novel Inverse Synthetic Aperture Rada Imaging Method for Maneuvering Targets Based on Modified Chirp Fourier Transform

2018 ◽  
Vol 8 (12) ◽  
pp. 2443 ◽  
Author(s):  
Yakun Lv ◽  
Yongping Wang ◽  
Yanhong Wu ◽  
Hongyan Wang ◽  
Lei Qiu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Descent Method ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Gradient Descent Method ◽  
Minimum Entropy ◽  
Echo Signal ◽  
Maneuvering Targets ◽  
Imaging Results ◽  
Low Efficiency

When inverse synthetic aperture radar (ISAR) imaging maneuvers targets, the azimuth echo of the target scattering point causes a Doppler frequency time-varying problem, which leads to the blurring and defocusing of the imaging results. Traditional imaging methods struggle to meet the imaging requirements for maneuvering targets due to a poor imaging effect or low efficiency. Given these challenges, a modified chirp Fourier transform (MCFT) imaging method is proposed in this paper, based on the specific relationship between the target rotation parameters and the radar echo signal parameters. Firstly, discrete chirp Fourier transform is used to quickly estimate the target’s coarse rotation ratio. Then, the minimum entropy function and gradient descent method are used to calculate the target’s accurate rotation ratio. Finally, the azimuth focusing image is accomplished by performing MCFT once on the azimuth echo signal using the accurate rotation ratio. This method avoids estimating and separating the sub-echo components one-by-one, considerably improves the imaging speed, and guarantees the best imaging quality by applying the global minimum entropy principle. The experimental results show that the proposed method effectively achieves the two-dimensional, high-quality, and fast imaging of maneuvering targets.

scholarly journals An Inverse Synthetic Aperture Ladar Imaging Algorithm of Maneuvering Target Based on Integral Cubic Phase Function-Fractional Fourier Transform

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 148 ◽  
Author(s):  
Yakun Lv ◽  
Yanhong Wu ◽  
Hongyan Wang ◽  
Lei Qiu ◽  
Jiawei Jiang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Phase Function ◽  
Fractional Fourier Transform ◽  
Doppler Frequency ◽  
Synthetic Aperture ◽  
Cubic Phase ◽  
Echo Signal ◽  
Imaging Algorithm ◽  
Maneuvering Target ◽  
Cubic Phase Function

When imaging maneuvering targets with inverse synthetic aperture ladar (ISAL), dispersion and Doppler frequency time-variation exist in the range and cross-range echo signal, respectively. To solve this problem, an ISAL imaging algorithm based on integral cubic phase function-fractional Fourier transform (ICPF-FRFT) is proposed in this paper. The accurate ISAL echo signal model is established for a space maneuvering target that quickly approximates the uniform acceleration motion. On this basis, the chirp rate of the echo signal is quickly estimated by using the ICPF algorithm, which uses the non-uniform fast Fourier transform (NUFFT) method for fast calculations. At the best rotation angle, the range compression is realized by FRFT and the range dispersion is eliminated. After motion compensation, separation imaging of strong and weak scattering points is realized by using ICPF-FRFT and CLEAN technique and the azimuth defocusing problem is solved. The effectiveness of the proposed method is verified by a simulation experiment of an aircraft scattering point model and real data.

scholarly journals A 3-D SAR Imaging Method Based on Back Projection Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032050
Author(s):  
Qian Han ◽  
Pengbo Wang ◽  
Xinkai Zhou ◽  
Xinchang Hu ◽  
Yanan Guo
Keyword(s):  
3D Imaging ◽  
Bp Algorithm ◽  
Projection Algorithm ◽  
Imaging Method ◽  
Back Projection ◽  
Imaging Processing ◽  
Imaging Algorithm ◽  
Imaging Results ◽  
2D Imaging ◽  
Low Efficiency

Abstract 3D back projection (BP) algorithm is an imaging algorithm based on time domain echo data, which effectively solves the overlapping mask problem existing in 2D SAR. It can complete the imaging processing of echo signal under any geometry configuration, and has the advantages of high target focusing accuracy and high phase preservation. However, the high complexity and low efficiency of 3D BP imaging algorithm limit its application and development. In this paper, a 3d imaging method based on improved back projection algorithm is proposed. Aiming at the problem that existing imaging algorithms need 2D imaging first and then 3D imaging, an improved 3D BP algorithm is proposed to directly 3D imaging, which avoids 2d imaging processing. The proposed method simplifies the steps of the traditional 3D BP algorithm and improves the efficiency of the algorithm. The validity and effectiveness of the proposed method are verified by the 3d imaging results of simulated lattice targets.

scholarly journals The Power Cepstrum Calculation with Convolutional Neural Networks

2019 ◽  
Vol 19 (2) ◽  
pp. e13 ◽  
Author(s):  
Mario Alejandro García ◽  
Eduardo Atilio Destéfanis
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Gradient Descent ◽  
Descent Method ◽  
Intermediate Step ◽  
Gradient Descent Method ◽  
The Neural Network ◽  
Regression Problems ◽  
The One ◽  
Deep Learning Model

A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. The weights of the neural network can be calculated in a direct way or they can be obtained through training with the gradient descent method. The behaviour of the training is analysed. The model originally proposed cannot be trained in a complete way, but both the part that calculates the spectrogram and also a variant of the cepstrum equivalent to the autocovariance that keeps a big part of its usefulness can be trained. For the cases of successful training, an analysis of the obtained weights is done. The main conclusions indicate, on the one hand, that it is possible to calculate the power cepstrum with a neural network; on the other hand, that it is possible to use these networks as the initial layers of a deep learning model for the case of trainable models. In these layers, weights are initialised with the discrete Fourier transform (DFT) coefficients and they are trained to adapt to specific classification or regression problems.

scholarly journals Extended Subaperture Imaging Method for Airborne Low Frequency Ultrawideband SAR Data

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4516
Author(s):  
Daoxiang An ◽  
Wu Wang ◽  
Leping Chen
Keyword(s):  
Low Frequency ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Theory Analysis ◽  
Range Cell ◽  
Imaging Results ◽  
Sar Imaging ◽  
Sar Data ◽  
Range Cell Migration

The subaperture processing is one of the essential strategies for low frequency ultrawideband synthetic aperture radar (LF UWB SAR) imaging, especially for the real-time LF UWB SAR imaging because it can improve the parallelization of the imaging algorithm. However, due to the longer synthetic aperture of LF UWB SAR, the traditional subaperture imaging encounters an azimuth ambiguities problem, which severely degrades the focused quality of the imaging results. In this paper, the reason for the presence of azimuth ambiguities in the LF UWB SAR subaperture imaging and its influence on image quality is first analyzed in theory. Then, an extended subaperture imaging method based on the extension of subaperture length before Range Cell Migration Correction (RCMC) was proposed. By lengthening the subaperture length, the azimuth ambiguities are effectively eliminated. Finally, the extended part of subaperture is wiped off before the azimuth compression (AC), and the LF UWB SAR image of high focused quality is obtained. The correctness of the theory analysis and the effectiveness of the proposed method have been validated through simulated and real LF UWB SAR data.

scholarly journals An Iterative Phase Autofocus Approach for ISAR Imaging of Maneuvering Targets

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2100
Author(s):  
Binbin Wang ◽  
Hao Cha ◽  
Zibo Zhou ◽  
Huatao Tang ◽  
Lidong Sun ◽  
...  
Keyword(s):  
Simple Procedure ◽  
Phase Error ◽  
Translational Motion ◽  
Minimum Entropy ◽  
New Approach ◽  
Maneuvering Targets ◽  
Quadratic Phase ◽  
Imaging Results ◽  
Isar Imaging ◽  
Maneuvering Motion

Translational motion compensation and azimuth compression are two essential processes in inverse synthetic aperture radar (ISAR) imaging. The anterior process recovers coherence between pulses, during which the phase autofocus algorithm is usually used. For ISAR imaging of maneuvering targets, conventional phase autofocus methods cannot effectively eliminate the phase error due to the adverse influence of the quadratic phase terms caused by the target’s maneuvering motion, which leads to the blurring of ISAR images. To address this problem, an iterative phase autofocus approach for ISAR imaging of maneuvering targets is proposed in this paper. Considering the coupling between translational phase errors and quadratic phase terms, minimum entropy-based autofocus (MEA) method and adaptive modified Fourier transform (MFT) are performed iteratively to realize better imaging results. In this way, both the translational phase error and quadratic phase terms induced by target’s maneuvering motion can be compensated effectively, and the globally optimal ISAR image is obtained. Comparison ISAR imaging results indicates that the new approach achieves stable and better ISAR image under a simple procedure. Experimental results show that the image entropy of the proposed approach is 0.2 smaller than the MEA method, which validates the effectiveness of the new approach.

scholarly journals SAR Image Formation Method with Azimuth Periodically Missing Data Based on RELAX Algorithm

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 49
Author(s):  
Weixing Yang ◽  
Daiyin Zhu
Keyword(s):  
Missing Data ◽  
Data Processing ◽  
Similarity Index ◽  
Structural Similarity ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Echo Signal ◽  
Structural Similarity Index ◽  
Recovery Method ◽  
Formation Method

Synthetic aperture radar (SAR) is a widely used remote sensing observation technique. However, SAR raw echo data may be lost during the process of data acquisition by radar platform. In this paper, the imaging problem of SAR echo signal with periodically missing data along the azimuth is analyzed and a novel imaging method is proposed. Firstly, the problem of artificial artifact targets caused by periodically missing data is explained in detail, and the corresponding mathematical model is established. Then, the recovery method based on the RELAX algorithm with periodic notches data is proposed. In addition, when the size of two-dimensional (2D) echo data are large, block restoration along the azimuth is proposed to reduce the amount of calculation. Finally, the advantages of the algorithm proposed in this paper is demonstrated by the points target simulated SAR echo data processing and the real raw SAR echo data processing. When the azimuth periodically missing data rate is 50%, the SAR echo data can be recovered and the well-focused image can be obtained. Comparing the image entropy value and structural similarity index (SSIM) of the focused image, it proves the superiority of the proposed algorithm in solving the imaging problem of SAR azimuth periodically missing data.

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