Studies of the angular correlation function of scattering by random rough surfaces with and without a buried object
- Univ. of Washington, Seattle, WA (United States)
The discrimination of the scattered wave from an object buried in shallow ground from that of the rough surface is a difficult task with present ground penetrating radar (GPR) systems. Recently, a new approach for this classical problem has been proposed and its effectiveness has been verified. This new method is based on the angular correlation function (ACF) of the scattered wave observed at two or more difficult incident and scattered angle combinations. It has been shown that the angular memory signatures of rough surfaces are substantially different from those of typical man-made targets and by choosing the appropriate incident and scattered angles, the surface scattering can be minimized whereas the scattering from the target is almost unchanged. In this paper, the authors will present detailed numerical studies of the ACF of the scattered wave from rough surfaces with and without a buried object. To obtain the ACF, the three averaging methods: realization, frequency and angular averaging, are tested numerically. It is shown that a single random rough surface of moderate extent can exhibit memory effect by using frequency averaging. Frequency averaging with a wide bandwidth is also effective for suppressing fluctuation in ACF and is most useful for practical applications. Numerical simulations indicate that even when the ratio of scattered intensities with and without the buried object is close to unity, the corresponding ratio of ACF magnitude can be more than 10 dB. Thus, using the ACF is superior to using the radar cross section (RCS) in the detection of buried objects.
- Sponsoring Organization:
- Office of Naval Research, Washington, DC (United States); National Science Foundation, Washington, DC (United States)
- OSTI ID:
- 464878
- Journal Information:
- IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Journal Issue: 2 Vol. 35; ISSN IGRSD2; ISSN 0196-2892
- Country of Publication:
- United States
- Language:
- English
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