Feature parameter extraction approach with the stealth S-Cubed radar signal

Radar equipments of stealth platforms such as aircrafts have adopted the newest modern technology to design the signal waveforms. One of the important and effective methods is the hybrid stealth radar waveform called spread spectrum stretch (S-Cubed). In this paper, the model of S-Cubed which combines linear frequency modulation (LFM) and discrete phase code is presented. Then, a novel approach based on phase difference and clustering is proposed for the feature parameter extraction of the intercepted S-Cubed. Simulation results show that the proposed algorithm has higher parameter extraction accuracy when the SNR is above 10dB.

[1]  Yingning Peng,et al.  Joint parameters estimation scheme for nonlinear frequency modulated radars , 2001, 2001 CIE International Conference on Radar Proceedings (Cat No.01TH8559).

[2]  David Lynch,et al.  Introduction to RF stealth , 2004 .

[3]  William A. Gardner,et al.  Signal-selective time-difference-of-arrival estimation for passive location of man-made signal sources in highly corruptive environments. I. Theory and method , 1992, IEEE Trans. Signal Process..

[4]  Gong Jing.  The Phase Difference Method for Power Quality Disturbance Signals Detection Based on Complex Wavelet Transform , 2009, 2009 International Conference on Energy and Environment Technology.

[5]  Gerald Matz,et al.  Time-Frequency ARMA Models and Parameter Estimators for Underspread Nonstationary Random Processes , 2007, IEEE Transactions on Signal Processing.

[6]  A. Napolitano,et al.  On the spectral correlation measurement of nonstationary stochastic processes , 2001, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).

[7]  William A. Gardner,et al.  Detection and source location of weak cyclostationary signals: simplifications of the maximum-likelihood receiver , 1993, IEEE Trans. Commun..

[8]  Gerald Matz,et al.  Least-Squares and Maximum-Likelihood Tfar Parameter Estimation for Nonstationary Processes , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[9]  G.B. Giannakis,et al.  Frequency offset and timing estimation in slowly-varying fading channels: a cyclostationary approach , 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

[10]  F. Vatalaro,et al.  Parameter estimation in CDMA multiuser detection using cyclostationary statistics , 1996 .

[11]  W.A. Gardner,et al.  Signal-selective time-difference of arrival estimation for passive location of man-made signal sources in highly corruptive environments. II. Algorithms and performance , 1992, IEEE Trans. Signal Process..