Incremental small sphere and large margin for online recognition of communication jamming
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Incremental small sphere and large margin for online recognition of communication jamming Yu Guo1 · Jin Meng1 · Yaxing Li1 · Songhu Ge1 · Jinling Xing1 · Hao Wu1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In the anti-jamming field of radio communication, the problem of online and multiclass jamming recognition is fundamental to implement reasonable anti-jamming measures. The incremental small sphere and large margin (IncSSLM) is proposed, this model can learn the compact boundary for own communication signals and known jamming, which relieves the openset problem of radio data. Meanwhile it can also update the model of classifier in real time, which avoids the large memory requirement for vast jamming data and saving much time for training. The core of proposed method is the small sphere and large margin (SSLM) approach, which makes the spherical area as compact as possible, like support vector data description (SVDD), and also makes the margin between them as far as possible, like support vector machine (SVM). In other words, it can minimize intra-class divergence and maximize inter-class space. Therefore, there is a significant enhancement of recognition performance when compared with open classifiers such as SVM, and considerable superiority of training efficiency when compared with the canonical SSLM algorithm. Numerical experiments based on synthetic data, practical complex feature data of high-resolution range profile (HRRP), and jamming data of radio communication demonstrate that IncSSLM is efficient and promising for multiple and online recognition of vase and open-set radio jamming. Keywords Anti-jamming · Small sphere and large margin · Support vector data description · Support vector machine · Online recognition
1 Introduction Recently, the online recognition of jamming has attracted intensive attentions from the machine learning and communication community [1]. Because the learning scenario where new examples are obtained and added after learning the original boundary using existing examples is general. The jamming recognition of radio communication is typical case where the modes of known jamming is much
Yaxing Li
[email protected] Yu Guo [email protected] Jin Meng [email protected] 1
National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan, 430000, China
limited and new examples are frequently obtained [2]. Convolutional neural network is used for jamming signal classification but unable to deal with unknown jamming and open-set problem of jamming [3]. Azami et al adoptd weighted support vector data description (SVDD) for outlier detection [4]. Wang et al adopted support vector machine (SVM) for jamming classification, similarly, the classifier is not updated timely when obtaining new examples [5]. How to update the recognition model efficiently and overcome the open-set problem of jamming recognition is a great challenge, and it is also the prerequisite of communication anti-jamming [
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