American Journal of Engineering and Applied Sciences

Analysis of Low Probability of Intercept Radar Signals Using the Reassignment Method

Daniel L. Stevens and Stephanie A. Schuckers

DOI : 10.3844/ajeassp.2015.26.47

American Journal of Engineering and Applied Sciences

Volume 8, Issue 1

Pages 26-47

Abstract

Digital intercept receivers are currently moving towards classical time-frequency analysis techniques for analyzing low probability of intercept radar signals. Although these techniques are an improvement over existing Fourier-based techniques, they still suffer from a lack of readability, due to poor time-frequency localization and cross-term interference, which may lead to inaccurate detection and parameter extraction. In this study, the reassignment method, because of its ability to smooth out cross-term interference and improve time-frequency localization, is proposed as an improved signal analysis technique to address these deficiencies. Simulations are presented that compare time-frequency representations of the classical time-frequency techniques with those of the reassignment method. Four different low probability of intercept waveforms are analyzed (two frequency modulated continuous wave waveforms and two frequency shift keying waveforms). The following metrics are used for analysis evaluation: Percent detection, number of cross-term false positives, lowest signal-to-noise ratio for signal detection, processing time, percent error of: Carrier frequency, modulation bandwidth, modulation period, time-frequency localization (x and y direction) and chirp rate. Experimental results demonstrate that the ‘squeezing’ and ‘smoothing’ qualities of the reassignment method improve readability over the classical time-frequency analysis techniques and consequently, provide more accurate signal detection and parameter extraction in all but one of the metrics categories.

Copyright

© 2015 Daniel L. Stevens and Stephanie A. Schuckers. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.