Radar emitter signal recognition based on atomic decomposition
Ming Zhu, Weidong Jin, Laizhao Hu · 2008
In this paper, a novel approach based on Gaussian Chirplet Atoms is presented to automatically recognise radar emitter signals. Firstly, based on the over-completed dictionary of Gaussian Chirplet atoms, the improved matching pursuit (MP) algorithm is applied to extract the features of the time-frequency atoms from the typical radar emitter signals, and FFT is introduced to effectively reduce the time complexity of searching step of MP. Secondly, reduce dimension of the feature parameters to re-extract the classification feature vectors. Finally, adopt the hierarchy decision strategy to realize automatic classification. The simulation experiment result shows that the classification feature vector has good properties of clustering the same and separating the different kind of radar emitter signals. Over 90% recognition accuracy can be achieved as the signal-to-noise ratio is greater than -4dB. Therefore, the approach of signal recognition is feasible in the practical engineering area.