Incorporation of aircraft orientation into automatic target recognition using passive radar

Ehrman, Lisa M.; Lanterman, Aaron D.

doi:10.1049/iet-rsn.2020.0010

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…Radar scattering cross section 1,2 , is a physical quantity to measure the intensity of the echo generated by the target under the irradiation of radar waves 3 . Radar cross section and echo intensity have important applications in stealth technology 7,8 , target recognition and tracking 9 , signal processing 10,11,12 : The reduction of target RCS can dimish the detection probability of the target in the radar system and improve the stealth performance of the target 4 . The intensity of echo determines the clarity of target recognition 13 .…”

Section: Introductionmentioning

confidence: 99%

Research on echo intensity characteristics of moving targets detection under atmospheric conditions

Zeng,

Chen

2024

Advanced Fiber Laser Conference (AFL2023)

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A method of detecting the echo intensity characteristics of moving targets under atmospheric conditions is proposed based on the atmospheric transmission model, atmospheric turbulence model and electromagnetic transmission theory in this paper. By setting light source and target parameters, the influences of atmospheric turbulence condition, target shape, incidence angle and detection condition on echo characteristics are obtained. Simulation results indicate that the echo intensity changes with different target shapes and incident angles. The absence of atmospheric turbulence conditions cause a more uniform variation in echo intensity compared to the presence of atmospheric turbulence conditions. Images gained from the study can invert the general shape of target. These provide a theoretical foundation for improving the probability of target recognition and detection accuracy.

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Section: Introductionmentioning

confidence: 99%

Research on echo intensity characteristics of moving targets detection under atmospheric conditions

Zeng,

Chen

2024

Advanced Fiber Laser Conference (AFL2023)

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“…Aiming at the shortcomings of traditional target detection methods, some scholars proposed a target detection method based on deep learning to obtain candidate target regions from images. It is found that the image target detection algorithm based on candidate target window is not ideal in feature extraction and recognition efficiency, and the image target classification is not accurate enough [3]. After the rapid development in recent years, the target detection algorithm has developed from the traditional manual feature detection algorithm to the current target detection algorithm based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

Image Target Detection and Recognition Method Using Deep Learning

Sun

2022

Advances in Multimedia

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Image target detection and recognition had been widely used in many fields. However, the existing methods had poor robustness; they not only had high error rate of target recognition but also had high dependence on parameters, so they were limited in application. Therefore, this paper proposed an image target detection and recognition method based on the improved R-CNN model, so as to detect and recognize the dynamic image target in real time. Based on the analysis of the existing theories of deep learning detection and recognition, this paper summarized the composition and working principle of the traditional image target detection and recognition system and compared the basic models of target detection and recognition, such as R-CNN network, Fast-RCNN network, and Faster-RCNN network. In order to improve the accuracy and real-time performance of the model in image target detection and recognition, this paper adopted the target feature matching module in the existing R-CNN network model, so as to obtain the feature map close to the same target through similarity calculation for the features extracted by the model. Therefore, an image target detection and recognition algorithm based on the improved R-CNN network model is proposed. Finally, the experimental results showed that the image target detection and recognition algorithm proposed in this paper can be better applied to image target detection and classification in complex environment and had higher detection efficiency and recognition accuracy than the existing models. The target detection and recognition algorithm proposed in this paper had certain reference value and guiding significance for further application research in related fields.

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“…The accuracy of correctly recognizing targets for different signal-to-noise ratios (SNR) and different viewing angles was assessed. Accuracy detection of targets for different SNRs (−20, −15, −10, −5, 0, 5, 10, 15, 20) and different viewing angles (10,20,30,40, 50, 60, 70, 80) is evaluated. For a more fair comparison, multilayer perceptron neural network with two back-propagation (MLP-BP) training methods and gray wolf optimization (MLP-GWO) algorithm were used.…”

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confidence: 99%

Using SVM Classifier and Micro-Doppler Signature for Automatic Recognition of Sonar Targets

2023

Archives of Acoustics

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In this paper, we propose using a propeller modulation on the transmitted signal (called sonar micro-Doppler) and different support vector machine (SVM) kernels for automatic recognition of moving sonar targets. In general, the main challenge for researchers and craftsmen working in the field of sonar target recognition is the lack of access to a valid and comprehensive database. Therefore, using a comprehensive mathematical model to simulate the signal received from the target can respond to this challenge. The mathematical model used in this paper simulates the return signal of moving sonar targets well. The resulting signals have unique properties and are known as frequency signatures. However, to reduce the complexity of the model, the 128point fast Fourier transform (FFT) is used. The selected SVM classification is the most popular machine learning algorithm with three main kernel functions: RBF kernel, linear kernel, and polynomial kernel tested. The accuracy of correctly recognizing targets for different signal-to-noise ratios (SNR) and different viewing angles was assessed. Accuracy detection of targets for different SNRs (−20, −15, −10, −5, 0, 5, 10, 15, 20) and different viewing angles (10,20,30,40, 50, 60, 70, 80) is evaluated. For a more fair comparison, multilayer perceptron neural network with two back-propagation (MLP-BP) training methods and gray wolf optimization (MLP-GWO) algorithm were used. But unfortunately, considering the number of classes, its performance was not satisfactory. The results showed that the RBF kernel is more capable for high SNRs (SNR = 20, viewing angle = 10) with an accuracy of 98.528%.

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Incorporation of aircraft orientation into automatic target recognition using passive radar

Cited by 7 publications

References 35 publications

Research on echo intensity characteristics of moving targets detection under atmospheric conditions

Research on echo intensity characteristics of moving targets detection under atmospheric conditions

Image Target Detection and Recognition Method Using Deep Learning

Using SVM Classifier and Micro-Doppler Signature for Automatic Recognition of Sonar Targets

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