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Automatic Radar Waveform Recognition Using SVM
Applied Mechanics and Materials, 2012In this paper, a new feature for radar waveform recognition based on the instantaneous frequency is proposed. It is especially utilized for discriminating phase coded signals from other signals. Maximum likelihood estimation (MLE), autocorrelation algorithm, and likelihood ratio test are exploited in the algorithm. In the classification system, support
Hao Gao, Xu Dong Zhang
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Towards an accurate radar waveform recognition algorithm based on dense CNN
Multimedia Tools and Applications, 2020Existing algorithms for radar waveform classification currently exhibit the lower recognition accuracy, especially at the lower signal to noise ratio (SNR) environment. To remedy these flaws, this paper proposes an accurate automatic modulation classification algorithm based on dense convolutional neural networks (AAMC-DCNN).
Weijian Si, Chenxia Wan, Chunjie Zhang
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Relationship of target recognition performance and radar waveform parameters
Journal of Electronics (China), 2011Target recognition performance can be affected by radar waveform parameters. In this paper, we established rigorous relationship between target recognition efficiency and the parameters of a repeatedly transmitted waveform. It is based on Kullback-Leibler Information Number of single observation (KLINs), which measures the dissimilarity between targets
Meimei Fan +4 more
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Radar Waveform Recognition based on Deep Residual Network
2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2019This article presents our initial results in deep learning for the complex multiple radar waveforms recognition. The method is composed of time-frequency analysis and deep residual network (ResNet). Firstly, we transform one-dimensional radar signals into two-dimensional time-frequency images (TFIs), which can reveal more characteristics of the signals.
Xin Qin +3 more
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Radar Signal Waveform Recognition Based on Convolutional Denoising Autoencoder
2019To solve the problem of the low recognition rate of the existing methods at low signal-to-noise ratio (SNR), we propose a novel method of radar signal waveform recognition. In this method, we extract the time-frequency images (TFIs) of radar signals through Cohen class time frequency distribution. Then, we introduce convolutional denoising autoencoder (
Zhaolei Liu, Xiaojie Mao, Zhian Deng
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Low Probability of Intercept Radar Waveform Recognition Based on Dictionary Leaming
2018 10th International Conference on Wireless Communications and Signal Processing (WCSP), 2018Low probability of intercept (LPI) radar waveform recognition is a challenging task in modern radar and electronic warfare (EW) systems. To solve the problem of incomplete information and the need for human experience in the existing feature-based radar recognition methods, a robust and automatic LPI radar waveform recognition method based on Choi ...
Huan Wang, Ming Diao, Lipeng Gao
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Use of wideband waveforms for target recognition with surveillance radars
Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037], 2002A long range target recognition system has been developed using a high range resolution mode in a surveillance radar. The radar performs normal air surveillance but on targets of interest a wideband waveform is used in a few pulses of the normal dwell. These pulses are processed to yield high resolution range profiles of the target.
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Automatic Radar Waveform Recognition Based on Deep Convolutional Denoising Auto-encoders
Circuits, Systems, and Signal Processing, 2018Aimed at the deficiency of traditional feature extraction techniques in radar emitter recognition, a novel deep feature extraction and recognition architecture is proposed. To fit into the model, the time-domain emitters are transformed into unique time-frequency images correspondingly.
Zhiwen Zhou +3 more
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Deep Learning for Coexistence Radar-Communication Waveform Recognition
2021 International Conference on Information and Communication Technology Convergence (ICTC), 2021Thien Huynh-The +3 more
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Analysis of Human Echolocation Waveform for Radar Target Recognition
2013Some blind humans have developed the remarkable capability of echolocation, similar to the type used by mammals such as the bat, dolphin and whale. This population of human has shown the ability to classify targets based on their location, size, shape and material in diverse environmental conditions simply by listening to the reflected echoes of tongue
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