Radar emitter signal recognition based on ambiguity function multi-domain feature fusion and ensemble learning
Aiming at the problems of poor anti-noise performance and low recognition accuracy of the radar emitter signal recognition method in the complex electromagnetic environment.An integrated deep learning recognition method based on multi-domain projection features of ambiguity function is proposed.Firstly,an ambiguity function is processed using a Gaussian operator,and the appropriate angle is selected to carry out two-dimensional projection to build a characteristic data set from the multi-domain perspective.Then,a two-stage recognition and classification method based on multi domain feature fusion is constructed.Multiple dense connected networks DenseNet 121 are used as primary classifiers to train and learn the three kinds of feature data sets respectively,and the primary classification results are obtained.Finally,the results of the primary classification are integrated through the Stacking policy to obtain the final classification result.The experimental results show that the overall average recognition rate of the six types of typical radar signals is above 97.24%,when the signal-to-noise ratio is 0 dB,even in the-4 dB environment,the recognition rate is also stable in 87.16%,which verifies the effectiveness and feasibility of the proposed method,and its certain engineering value.
NETL
NSTL
万方数据
维普
