基于高斯混合模型的辐射源模式识别算法

Gaussian Mixture Model Based Algorithm for Radiator Pattern Recognition

针对现有算法对战场新出现辐射源学习与分类能力较差的问题,提出了基于高斯混合模型(Gaussian Mixture Model,GMM)的辐射源信号模式识别算法,该算法对信号在特征空间中的分布模式进行在线学习,形成基于概率统计的分类模型,在分类中给出样本归属的似然概率。为了进一步提高算法的实时性,提出基于空间网格划分的快速EM(Expectation Maximization)方法,从而使GMM拟合的计算复杂度取决于网格划分的密度而不是样本数量,极大提高了算法效率。对电子侦察典型场景的仿真分析表明,算法能够对各类辐射源进行在线学习与分类,适应性较强,且计算效率较传统EM算法有较大提高。

To overcome the difficulties of learning and classifying newly emerged signal patterns,a Gaussian mixture model(GMM)based algorithm for radiator pattern recognition was proposed.The algorithm could perform online learning on the signal's distribution in the feature space,which formed a probabilitybased classification model which classified samples by likelihood.Besides,to match the realtime requirement of the system,a gridbased fast expectation maximization(EM)method was proposed,which made the caculation complexity of GMM fitting be propotional to the grid amount but not to the sample amount.Computer simulations showed that the proposed algorithm was capable of learning and classifying new signal patterns and the computational efficency had greatly improved compared with traditional EM method.