基于SAMME+ResNet的多相码信号识别方法

针对传统多相码信号识别方法在低信噪比情况下分类精度不高、类识别率不均衡和识别方法不具有通用性的特点,提出了一种利用集成学习中的多类指数损失函数逐步添加模型(stagewise additive modeling using a multi-class exponential loss function, SAMME)算法和残差神经网络(residual neural network, ResNet)的多相码信号识别方法。通过仿真实验对5类多相码信号进行了分类识别,验证了模型的有效性,分析了不同数量基学习器对模型的影响,最后与传统分类方法进行了对比。仿真结果表明,在信噪比低于6 dB的情况下,所提方法相对于单个残差网络提高了约10%的分类精度,同时缩小了类之间识别率的差距,相对于常用的分类方法也有很大的优势。

Polyphase code signal recognition method based on SAMME+ResNet

In view of the characteristics of the traditional polyphase code signal recognition methods, such as low classification accuracy, uneven class recognition rate and non-generality of recognition methods in the case of low signal to noise ratio (SNR), a polyphase code signal recognition method based on stagewise additive modeling using a multi-class exponential loss function (SAMME) algorithm in ensemble learning and residual neural network (ResNet) is proposed. Simulation experiments are carried out to classify and identify five kinds of polyphase code signals, and the validity of the model is verified. The influence of different quantity base learners on the model is analyzed. Finally, the proposed method is compared with the traditional classification methods. Simulation results show that when the SNR is lower than 6 dB, the proposed method improves the classification accuracy by about 10% compared with single residual network and reduces the difference of recognition rate between classes and also has great advantages over common classification methods.