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 open-set 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.

在无线电通信的抗干扰领域，在线和多类干扰识别问题对于实施合理的抗干扰措施至关重要。提出了增量小球和大余量（IncSSLM），该模型可以学习自身通信信号和已知干扰的紧凑边界，从而缓解了无线电数据的开放集问题。同时还可以实时更新分类器的模型，避免了海量干扰数据的大内存需求，节省了训练时间。所提出方法的核心是小球和大余量（SSLM）方法，它使球形区域尽可能紧凑，如支持向量数据描述（SVDD），并且使它们之间的余量尽可能大，如支持向量机（SVM）。换一种说法，它可以最小化类内差异并最大化类间空间。因此，与开放式分类器（例如SVM）相比，识别性能有了显着提高，而与标准SSLM算法相比，则在训练效率上具有相当大的优势。基于合成数据，高分辨率范围轮廓（HRRP）的实用复杂特征数据和无线电通信干扰数据的数值实验表明，IncSSLM是有效的，并有望用于花瓶的多联机识别和开放式无线电干扰。