Results 181 to 190 of about 1,363 (214)
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Transferred deep learning based waveform recognition for cognitive passive radar
Signal Processing, 2019Abstract Passive radar capable of recognizing illumination of opportunities can improve the detection performance on account of its functional properties of environment adaptivity. Waveform recognition approaches based on Deep Learning can outperform traditional methods based on hand-crafted feature as shown in recent studies.
Panfei Du, Jingyu Yang, Guohua Wang
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Automatic target recognition using waveform diversity in radar sensor networks
Pattern Recognition Letters, 2008In this paper, we perform a number of theoretical studies on constant frequency (CF) pulse waveform design and diversity in radar sensor networks (RSN): (1) the conditions for waveform co-existence, (2) interferences among waveforms in RSN, (3) waveform diversity combining in RSN. As an application example, we apply the waveform design and diversity to
Qilian Liang
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Analysis of Human Echolocation Waveform for Radar Target Recognition [PDF]
, 2013 Some 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
Patel, Kandarp
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An Improved LPI Radar Waveform Recognition Framework With LDC-Unet and SSR-Loss
IEEE Signal Processing Letters, 2022, Mengmeng Liao, Wangkui Jiang
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Efficient TFI-Based Depth-Tunable LPI Radar Waveform Recognition Network
IEEE Signal Processing Letters, 2023Zhiyong Luo, Wang Xiti
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Accurate Deep CNN-Based Waveform Recognition for Intelligent Radar Systems
IEEE Communications Letters, 2021Nowadays radar systems have been facing with the disordered electromagnetic spectrum access and utilization in shared spectrum environments with radio communication systems. Numerous waveform recognition methods have been studied with feature engineering and conventional machine learning (ML) for intelligent radar systems, but they are critically ...
Thien Huynh-The +4 more
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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 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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