Title :
Method for feature extraction of radar full pulses based on EMD and chaos detection
Author :
Qiang Guo ; Pulong Nan
Author_Institution :
Coll. of Inf. & Commun. Eng., Harbin Eng. Univ., Harbin, China
Abstract :
A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.
Keywords :
Gaussian noise; chaos; feature extraction; radar signal processing; Duffing equation; EMD detection; Gaussian noise insertion; RF; TOA; chaos detection; chaos detection model; electromagnetic environment; empirical mode decomposition; feature extraction method; frequency slippage signal extraction; information sequence; radar full pulse sequence; radar interception receiver; radio frequency; signal features; structure function series; time of arrival; Correlation; Empirical mode decomposition; Feature extraction; Mathematical model; Noise measurement; Radio frequency; Time series analysis; Duffing equation; Gaussian noise insertion; empirical mode decomposition; feature extraction; structure function;
Journal_Title :
Communications and Networks, Journal of
DOI :
10.1109/JCN.2014.000012