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

Hu, Rongchun; Peng, Zhenming; Ma, Jun

doi:10.3390/electronics8111252

Cited by 7 publications

(11 citation statements)

References 39 publications

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“…The contour thinning algorithm was proposed in [34] and has been used in vehicle target recognition for CSAR [53]. This algorithm is based on the backprojection algorithm.…”

Section: Analysis Of Contour Thinning Imagingmentioning

confidence: 99%

“…This dataset is a subset of the Gotcha dataset, which was trimmed from a large scene, including 33 civilian vehicles and some auxiliary reflectors [19]. Since then, more researchers have begun to study target recognition with WSAR [45][46][47][48][49][50][51][52][53][54]. These target recognition algorithms, including feature set and template matching-based, manifold learning-based, and deep learning-based algorithms, have achieved good results.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm combines imaging and target recognition. Based on contour thinning imaging, the authors also proposed a target recognition algorithm using feature set matching [53]. The contour thinning imaging algorithm significantly improved the target's recognizability, and achieved good results in vehicle recognition.…”

Section: Introductionmentioning

confidence: 99%

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

Peng

et al. 2020

Electronics

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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.

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“…The contour thinning algorithm was proposed in [34] and has been used in vehicle target recognition for CSAR [53]. This algorithm is based on the backprojection algorithm.…”

Section: Analysis Of Contour Thinning Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Peng

et al. 2020

Electronics

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“…A synthetic aperture radar (SAR) has all-weather, all-day, high-resolution and multi-dimensional imaging capabilities [1][2][3][4][5][6][7]. The traditional linear array SAR (LA-SAR) [8][9][10][11] can only observe the front area of the antenna array unit from a single angle of view. Compared with LA-SAR, arc-array SAR (AA-SAR) [12][13][14][15][16][17][18][19][20][21] is a novel array microwave imaging mode with full azimuth observation, which breaks through the limitation of the single observation angle of conventional LA-SAR.…”

Section: Introductionmentioning

confidence: 99%

Focusing Arc-Array Bistatic Synthetic Aperture Radar Data Based on Keystone Transform

Huang

et al. 2019

Electronics

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Arc-array synthetic aperture radar (AA-SAR) has become a novel imaging scheme for full azimuth observation. However, the exiting arc-array monostatic SAR system is placed on a single platform and is easy to detect. Arc-array bistatic SAR (AA-BiSAR) with a stationary transmitter is proposed in this paper, which can obtain high data acquisition efficiency and reduce vulnerability of arc-array monostatic SAR. Furthermore, since the azimuth resolution with full azimuth observation is not related to the location of the stationary transmitter, the transmitter can be placed far away from the receiver. Compared with imaging algorithms for other modes, the key points of AA-BiSAR imaging algorithms are a square root in the bistatic slant range equation and an arc synthetic array in azimuth. According to the imaging geometry of AA-BiSAR, a novel imaging approach for AA-BiSAR based on keystone transform (KT) is proposed, and the KT implements range-cell migration correction (RCMC) in conditions of trigonometric function under square root in the range history and arc synthetic array in azimuth via reformatting the AA-BiSAR raw data. Besides presenting the proposed imaging approach, a complete resolution analysis of AA-BiSAR is given. Results of numerical simulation experiments on point targets validate the proposed imaging approach.

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“…Radar is an important part of the contemporary intelligent transportation system [1][2][3]. Multi-target tracking with radar is also a hot issue in intelligent transportation research [4][5][6]. By tracking passing vehicles, risky driving behavior can be predicted and an early warning signal can be issued [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

et al. 2019

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The detection of adjacent vehicles in highway scenes has the problem of inaccurate clustering results. In order to solve this problem, this paper proposes a new clustering algorithm, namely Spindle-based Density Peak Fuzzy Clustering (SDPFC) algorithm. Its main feature is to use the density peak clustering algorithm to perform initial clustering to obtain the number of clusters and the cluster center of each cluster. The final clustering result is obtained by a fuzzy clustering algorithm based on the spindle update. The experimental data are the radar echo signal collected in the real highway scenes. Compared with the DBSCAN, FCM, and K-Means algorithms, the algorithm has higher clustering accuracy in certain scenes. The average clustering accuracy of SDPFC can reach more than 95%. It is also proved that the proposed algorithm has strong robustness in certain highway scenes.

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

Cited by 7 publications

References 39 publications

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

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

Focusing Arc-Array Bistatic Synthetic Aperture Radar Data Based on Keystone Transform

A Novel Density Peak Fuzzy Clustering Algorithm for Moving Vehicles Using Traffic Radar

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