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<title cf:type="text"><![CDATA[Editorial department of the Journal of National University of Defense Technology -->认知电子战技术]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Overview of cognitive electronic warfare]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305001]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Cognitive electronic warfare is usually defined as a form of electronic warfare that is based on electronic warfare equipment with cognitive performance and focuses on autonomous interactive electromagnetic environment learning capability and dynamic intelligent confrontation task processing capability. Since it was first proposed, it has attracted extensive attention from researchers and scholars at home and abroad for its advantages of accurate perception, strong reasoning and fast decision-making. With the continuous emergence of new concepts, technologies and applications of artificial intelligence, cognitive electronic warfare has stepped into a brand new stage of development. In order to capture its future development direction, the connotation of the concept of cognitive electronic warfare was summarized and enriched from the perspective of artificial intelligence, the development of cognitive electronic warfare and typical foreign projects were sorted out, the framework and architecture of cognitive electronic warfare system was built, a comprehensive and systematic review of the key technologies of cognitive electronic warfare was conducted from the aspects of perception, judgment, decision-making, etc., and the challenges and development trends of cognitive electronic warfare were summarized.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[认知电子战技术]]></category>
<author><![CDATA[HUANG Zhitao, WANG Xiang, ZHAO Yurui]]></author>
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<atom:name>HUANG Zhitao, WANG Xiang, ZHAO Yurui</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Specific emitter identification using reconstructed attractors]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305002]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In order to solve the problems of high dimension of reconstructed feature vector, low computational efficiency and poor robustness of existing phase space based individual recognition methods, SEI(specific emitter identification) framework based on reconstructed attractors was proposed from the perspective of nonlinear dynamics. Within the proposed framework, a novel SEI technology based on Isomap(isometric mapping) was developed. The technology used Isomap to reconstruct the emitter attractor from phase space, which can describe the dynamic characteristics of the emitter system in a lower dimension and reflect the “fingerprint” characteristics of the emitter individual. Experiments show that the proposed method can achieve higher accuracy, higher efficiency and better robustness.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[认知电子战技术]]></category>
<author><![CDATA[ZHAO Yurui, SONG Chuanjiang, WANG Xiang, HUANG Zhitao]]></author>
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<atom:name>ZHAO Yurui, SONG Chuanjiang, WANG Xiang, HUANG Zhitao</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Asynchronous and non-stationary interference mitigation method]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305003]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[To address the problem of mitigating asynchronous non-stationary interference in single-channel conditions, a data-driven sparse component analysis method was proposed. The aim of this method is to recover the desired signal from the received mixed signals. This method used the powerful modeling ability of deep convolutional neural network to model the complex mapping between the input and output data, and realized the adaptive selection of sparse domain of target signals, the adaptive learning of sparse representation of target signals in sparse domain, and the automatic recovery of target signals. Unlike the previous interference mitigation algorithms, the proposed method completed the “end-to-end” signal waveform recovery in the time domain, and had no prior requirement for aliasing observation, which was more universal than the existing methods. Simulation experiments verified the effectiveness of the proposed interference mitigation method under different environmental noise and interference signal strength and generalization test conditions, and the interference mitigation performance is significantly better than the existing algorithms.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[认知电子战技术]]></category>
<author><![CDATA[DENG Wen, HUANG Zhitao, WANG Xiang, DAI Dingchuan, CHEN Liangdong]]></author>
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<atom:name>DENG Wen, HUANG Zhitao, WANG Xiang, DAI Dingchuan, CHEN Liangdong</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Universal adversarial attack method for communication modulation identification using principal component analysis]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305004]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Deep learning is easily attacked by adversarial examples. Taking communication modulation recognition as an example, adding adversarial perturbations to the transmitted signal can effectively prevent non-cooperative users from utilizing the deep learning method to recognize the modulation of the signal. Thus, adversarial perturbations can help enhance communication security. To address the problem that the existing adversarial attack techniques are difficult to meet the adaptive and real-time requirements, the universal adversarial perturbation applicable to the whole dataset was obtained by the principal component analysis of the adversarial perturbation generated by a small part of the data extracted from the dataset. The computation of the universal adversarial perturbation can be carried out under offline conditions and then added to the signal to be transmitted in real time, which can satisfy the real-time requirements of communication and realize the purpose of reducing the accuracy of non-cooperative party modulation recognition. Experimental results show that the proposed method has better deception performance relative to the baseline method.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[认知电子战技术]]></category>
<author><![CDATA[KE Da, HUANG Zhitao, DENG Shouyun, LU Chaoqi]]></author>
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<atom:name>KE Da, HUANG Zhitao, DENG Shouyun, LU Chaoqi</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Convolutional codes recognition method based on joint learning of matrix transformation features and code sequences]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305005]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Existing deep learning based convolutional code recognition methods still have shortcomings such as large parameter sizes and weak recognition performance. Aiming at this problem, a convolutional code recognition method based on joint learning of matrix transform features and code sequences was proposed. The received codeword sequence was arranged into a matrix form, and the soft information was used to eliminate the codewords with low reliability. Then, a new matrix transformation algorithm was used to obtain the feature matrix. During the recognition process, the original matrix of code words and the matrix of features were fed into a network model with a joint learning capability for multimodal data. The feature extraction fusion and convolution code recognition were completed in the neural network. Simulation results show that the recognition performance of the proposed method is significantly better than the existing recognition methods based on deep learning, especially for high bit rate convolutional codes. When the rate is low, the proposed method is also better than traditional methods. When the signal-to-noise ratio reaches 5 dB, the recognition rate of 25 convolutional codes with different parameters can reach 100%.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[认知电子战技术]]></category>
<author><![CDATA[WANG Yao, MAN Xin, YOU Hongyu, MING Liang, LIU Weisong, HUANG Zhitao]]></author>
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<atom:name>WANG Yao, MAN Xin, YOU Hongyu, MING Liang, LIU Weisong, HUANG Zhitao</atom:name>
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