统计学习理论算法在跳频信号分选中的应用

在第3方截获并检测到跳频通信信号后,由于无先验知识,所以对其进行网台分选不便采用传统有监督学习算法.即便采用无监督的算法,目前多数算法对分类个数等相关消息也必须有所借鉴并在多分选参数的逐级分选中通过先验知识进行有效性判断和筛选.针对电子支援中探测到的跳频信号分选所遇到的困难,利用统计学习理论在小样本学习及非线性分类上较其它传统分类算法更好的性能,提出基于统计学习理论的无监督及半监督学习算法,对第3方得到的跳频网台分选进行应用,取得理想结果.为跳频通信侦察过程中的分选工作,提供一种应用鲁棒性好,分选准确度高的方法.

The Research on the Application of the Statistical Learning Theory Algorithm to the Hopping Frequency Signals' Separation

After the third party has captured and detected the frequency hopping (FH) communication signals, the lack of the priori knowledge leads to that the classic supervised learning algorithm is infeasible for separating HF signals. Even if, nowadays, many unsupervised algorithms are adopted there are still that the cluster number needs referring and multiple classification parameters separation by steps carries on the validity judging and filtering according to the priori knowledge. For the difficulties met in the course of the separation of FH signals detected in electronic support measures, we make use of the performance of the statistical learning theory (SLT), which is higher than that of the others in the small sample learning and the nonlinear classification to put forward unsupervised and semi-supervised learning algorithms based on SLT, by using which FH signals got by the third party are well classified. This research provides a kind of classification method that is of higher applicable robustness and higher accuracy rate for signals separation in FH communication reconnaissance.