Radar modulation classification using time-frequency representation and nonlinear regression

Christophe De Luigi; Pierre-Yves Arques; Jean-Marc Lopez; Eric Moreau

doi:10.1117/12.364082

29 September 1999 Radar modulation classification using time-frequency representation and nonlinear regression

Christophe De Luigi, Pierre-Yves Arques, Jean-Marc Lopez, Eric Moreau

Proceedings Volume 3810, Radar Processing, Technology, and Applications IV; (1999) https://doi.org/10.1117/12.364082
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

In naval electronic environment, pulses emitted by radars are collected by ESM receivers. For most of them the intrapulse signal is modulated by a particular law. To help the classical identification process, a classification and estimation of this modulation law is applied on the intrapulse signal measurements. To estimate with a good accuracy the time-varying frequency of a signal corrupted by an additive noise, one method has been chosen. This method consists on the Wigner distribution calculation, the instantaneous frequency is then estimated by the peak location of the distribution. Bias and variance of the estimator are performed by computed simulations. In a estimated sequence of frequencies, we assume the presence of false and good estimated ones, the hypothesis of Gaussian distribution is made on the errors. A robust non linear regression method, based on the Levenberg-Marquardt algorithm, is thus applied on these estimated frequencies using a Maximum Likelihood Estimator. The performances of the method are tested by using varied modulation laws and different signal to noise ratios.

Citation Download Citation

Christophe De Luigi, Pierre-Yves Arques, Jean-Marc Lopez, and Eric Moreau "Radar modulation classification using time-frequency representation and nonlinear regression", Proc. SPIE 3810, Radar Processing, Technology, and Applications IV, (29 September 1999); https://doi.org/10.1117/12.364082

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