Automatic classification of minelike targets buried underground using time-frequency signatures extracted by a stepped-frequency radar

Hans C. Strifors; Anders Gustafsson; Steffan Abrahamson; Guillermo C. Gaunaurd

doi:10.1117/12.445373

22 October 2001 Automatic classification of minelike targets buried underground using time-frequency signatures extracted by a stepped-frequency radar

Hans C. Strifors, Anders Gustafsson, Steffan Abrahamson, Guillermo C. Gaunaurd

Author Affiliations +

Proceedings Volume 4379, Automatic Target Recognition XI; (2001) https://doi.org/10.1117/12.445373
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

Ultra-wideband radar systems are feasible for extracting signature infor-ma-tion use-ful for target recognition purposes. An ultra-wideband radar system emits either an extremely short pulse, impulse, or a frequency modulated signal. The frequency content of the emitted signals is designed to match the size and kind of typical targets and environments. We investigate the backscattered echoes from selected targets that are extracted by a stepped-frequency continuous wave (SFCW) radar system playing the role of ground penetrating radar (GPR). The targets are metal and non-metal objects buried in dry sand. The SFCW radar transmits 55 different frequencies from 300 to 3,000 MHz in steps of 50 MHz. The duration of each frequency is about 100 µs, which means that each transmitted waveform has an extremely narrow band. The in-phase (I) sampled signals and quadrature-phase (Q) sampled signals give information of both the amplitude and phase of the signal returned from the target. As a result a complex-valued line spectrum of the target is obtained that can be used for synthesizing real-valued repetitive waveforms, using the inverse Fourier transform. We analyze synthe-sized back-scat-tered echoes from each target in the joint time-frequency domain us-ing a pseudo-Wigner distribution (PWD). A classification method that we developed previously using the fuzzy C-means clustering technique is then used to reduce the number and kind of fea-tures in the derived target signatures. Using a template for each member of the class the classifier decides the membership of a given target based on best fit of the templates measured by a cost function. We also address the problem of how to select suitable waveforms for the templates used by the classification algorithm.

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Hans C. Strifors, Anders Gustafsson, Steffan Abrahamson, and Guillermo C. Gaunaurd "Automatic classification of minelike targets buried underground using time-frequency signatures extracted by a stepped-frequency radar", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); https://doi.org/10.1117/12.445373

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KEYWORDS

Radar

Antennas

Time-frequency analysis

Metals

Target recognition

Signal detection

Target detection

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