Feature extraction of radar emitter signals based on timefrequency atoms
Laizhao Hu · Chinese Journal of Radio Science · 2007
A novel approach to extract the features of radar emitter signals in the high density and complex and variable signal modulation environment is presented in this paper.Based on the overcomplete timefrequency atom dictionary,the signals are decomposed into a linear expansion of atoms by the method of Matching Pursuit.Then,improved quantum genetic algorithm is applied to effectively reduce the timecomplexity at each search step of MP,and thus some optimal timefrequency atoms describing features of signals are obtained,which can provide some new feature parameters for the deinterleaving and recognition of the radar emitter signals subsequently.Experiment result proved the validity and feasibility of the approach and that the extracted atoms had the features of a certain extent noisesuppression ability.