Study Of Methods Of Radar Emitter Signal Detection And Inner-pulse Fine Feature Extraction

In modern electronic warfare, radar emitter recognition is one of important tasks in electronic intelligence reconnaissance system (ELINT) and electronic support measures (ESM). In this paper, radar emitter signal detection, preprocessing and feature extraction are studied respectively, so as to provide effective inner-pulse fine features for individual emitter recognition.For radar emitter signal detection, a cyclic-autocorrelation based signal detection algorithm is introduced first, which has better detection performance, but can not estimate the time parameters of detected signal. Further, a time-frequency detection algorithm based on S transform for complex modulation signal is proposed, which models hybrid spread spectrum signal and analyzes the statistical properties of S spectrum of Gaussian white noise in detail. Simulation results show that the algorithm can not only give existence detection of signals, but also realize the time parameter detection simultaneously.For radar emitter signal preprocessing, signal de-noising and alignment are first studied. Next, time domain mathematical model of multipath signals is derived, through analyzing the time-frequency distribution of real radar signals. Finally, multipath detection algorithm based on Fraction Fourier transform for emitter signal is proposed, whose effectiveness is verified by simulation results.For inner-pulse fine feature extraction of radar emitter signal, we studied several feature extraction algorithms in detail from time domain, frequency domain and transformation domain respectively, and integrated the logarithmic transformation into the wavelet packet energy based method and EMD time-frequency reconstruction method. Finally, based on experimental results on real radar data, the performance analyses of above feature extraction algorithms, as well as some suggestions for engineering application, are provided.