scholarly journals CNN-Based Vehicle Target Recognition with Residual Compensation for Circular SAR Imaging

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 555
Author(s):  
Rongchun Hu ◽  
Zhenming Peng ◽  
Juan Ma ◽  
Wei Li
Keyword(s):  
Recognition Accuracy ◽  
Target Recognition ◽  
Synthetic Aperture ◽  
Speckle Reduction ◽  
Reduction Algorithm ◽  
Target Image ◽  
Thinning Algorithm ◽  
Imaging Algorithm ◽  
Residual Image ◽  
Sar Imaging

The contour thinning algorithm is an imaging algorithm for circular synthetic aperture radar (SAR) that can obtain clear target contours and has been successfully used for circular SAR (CSAR) target recognition. However, the contour thinning imaging algorithm loses some details when thinning the contour, which needs to be improved. This paper presents an improved contour thinning imaging algorithm based on residual compensation. In this algorithm, the residual image is obtained by subtracting the contour thinning image from the traditional backprojection image. Then, the compensation information is extracted from the residual image by repeatedly using the gravitation-based speckle reduction algorithm. Finally, the extracted compensation image is superimposed on the contour thinning image to obtain a compensated contour thinning image. The proposed algorithm is demonstrated on the Gotcha dataset. The convolutional neural network (CNN) is used to recognize the target image. The experimental results show that the image after compensation has a higher target recognition accuracy than the image before compensation.

scholarly journals A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1252 ◽  
Author(s):  
Rongchun Hu ◽  
Zhenming Peng ◽  
Juan Ma
Keyword(s):  
Synthetic Aperture Radar ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Recognition Algorithm ◽  
Synthetic Aperture ◽  
Speckle Reduction ◽  
Wide Angle ◽  
Target Imaging ◽  
Standard Orientation ◽  
Aperture Radar

Target recognition is an important area in Synthetic Aperture Radar (SAR) research. Wide-angle Synthetic Aperture Radar (WSAR) has obvious advantages in target imaging resolution. This paper presents a vehicle target recognition algorithm for wide-angle SAR, which is based on joint feature set matching (JFSM). In this algorithm, firstly, the modulus stretch step is added in the imaging process of wide-angle SAR to obtain the thinned image of vehicle contour. Secondly, the gravitational-based speckle reduction algorithm is used to obtain a clearer contour image. Thirdly, the image is rotated to obtain a standard orientation image. Subsequently, the image and projection feature sets are extracted. Finally, the JFSM algorithm, which combines the image and projection sets, is used to identify the vehicle model. Experiments show that the recognition accuracy of the proposed algorithm is up to 85%. The proposed algorithm is demonstrated on the Gotcha WSAR dataset.

scholarly journals Image Formation of Azimuth Periodically Gapped SAR Raw Data with Complex Deconvolution

2019 ◽  
Vol 11 (22) ◽  
pp. 2698 ◽  
Author(s):  
Yulei Qian ◽  
Daiyin Zhu
Keyword(s):  
Complete Data ◽  
Synthetic Aperture ◽  
Phase Compensation ◽  
Raw Data ◽  
New Approach ◽  
Imaging Algorithm ◽  
Robust Implementation ◽  
Sar Imaging ◽  
Novel Method ◽  
Sar Data

The phenomenon of periodical gapping in Synthetic Aperture Radar (SAR), which is induced in various ways, creates challenges in focusing raw SAR data. To handle this problem, a novel method is proposed in this paper. Complex deconvolution is utilized to restore the azimuth spectrum of complete data from the gapped raw data in the proposed method. In other words, a new approach is provided by the proposed method to cope with periodically gapped raw SAR data via complex deconvolution. The proposed method provides a robust implementation of deconvolution for processing azimuth gapped raw data. The proposed method mainly consists of phase compensation and recovering the azimuth spectrum of raw data with complex deconvolution. The gapped data become sparser in the range of the Doppler domain after phase compensation. Then, it is feasible to recover the azimuth spectrum of the complete data from gapped raw data via complex deconvolution in the Doppler domain. Afterwards, the traditional SAR imaging algorithm is capable of focusing the reconstructed raw data in this paper. The effectiveness of the proposed method was validated via point target simulation and surface target simulation. Moreover, real SAR data were utilized to further demonstrate the validity of the proposed method.

scholarly journals High-Resolution Squinted SAR Imaging Algorithm Applied in the Near-Field Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shijia Wang ◽  
Shibo Wang ◽  
Wanli Liu
Keyword(s):  
Cell Migration ◽  
Near Field ◽  
Synthetic Aperture ◽  
Distortion Compensation ◽  
Imaging Algorithm ◽  
Field Environment ◽  
Imaging Result ◽  
Range Cell ◽  
Sar Imaging ◽  
Range Cell Migration

In the squinted synthetic aperture radar (SAR) imaging of the near-field environment, range-dependent characteristic of squint angle cannot be ignored, which causes azimuth-dependent range cell migration (RCM) after linear range walk correction (LRWC). In this study, an efficient SAR imaging algorithm applied in the near-field environment is proposed. In the processing of the range focusing, LRWC is firstly used to remove the linear RCM. Then, the residual LRCM is expanded into azimuth-invariant and azimuth-variant terms in consideration of the residual LRCM of azimuth-dependent. Range cell migration azimuth scaling (RCMAS) is designed to remove the azimuth-variant term before secondary range compression (SRC) and range compression (RC). In the azimuth focusing, azimuth distortion compensation (ADC) is performed to compensate the azimuth distortion, following which azimuth nonlinear chirp scaling (ANCS) is applied to equalize the frequency modulation (FM) rate for azimuth compression (AC). The simulated results show that more accurate and improved imaging result can be obtained with the proposed algorithm.

scholarly journals Modulus Stretch-Based Circular SAR Imaging with Contour Thinning

2019 ◽  
Vol 9 (13) ◽  
pp. 2728 ◽  
Author(s):  
Rongchun Hu ◽  
Zhenming Peng ◽  
Kelong Zheng
Keyword(s):  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Transformation Function ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Imaging Result ◽  
Target Imaging ◽  
Sar Imaging ◽  
Line Target ◽  
Subsequent Target

This paper presents a modulus stretch-based circular Synthetic Aperture Radar (SAR) imaging method. This method improves the traditional backprojection algorithm for circular SAR imaging, and introduces the modulus stretch transformation function in the imaging process. By performing a modulus stretch transformation on the intermediate results, the target contour in the final imaging result is thinner and clearer. A thinner and clearer contour can help to increase the recognizability of the target and provide a basis for subsequent target recognition. The proposed method is demonstrated on the line target imaging simulations and Gothca dataset.

A Study of Space-Borne Sliding Spotlight SAR Imaging Algorithm

2012 ◽  
Vol 429 ◽  
pp. 128-131
Author(s):  
Ying Ying Chen ◽  
Xin Jia
Keyword(s):  
Synthetic Aperture Radar ◽  
Operating Mode ◽  
Synthetic Aperture ◽  
Imaging Algorithm ◽  
Sar Imaging ◽  
Simulation Results ◽  
Azimuth Resolution ◽  
Aperture Radar

In recent years, a new operating mode for Synthetic Aperture Radar (SAR) system, that is sliding spotlight mode. This mode has many advantages, such as, it has better azimuth resolution than stripmap mode. At the same time, the sliding spotlight mode has better coverage than the spotlight mode. In this paper, we present the sliding spotlight mode algorithm and analyze its azimuth resolution and coverage. The simulation results prove it.

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