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Proceedings Paper

A bat inspired technique for clutter reduction in radar sounder systems
Author(s): L. Carrer; L. Bruzzone
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Paper Abstract

Radar Sounders are valuable instruments for subsurface investigation. They are widely employed for the study of planetary bodies around the solar system. Due to their wide antenna beam pattern, off-nadir surface reflections (i.e. clutter) of the transmitted signal can compete with echoes coming from the subsurface thus masking them. Different strategies have been adopted for clutter mitigation. However, none of them proved to be the final solution for this specific problem. Bats are very well known for their ability in discriminating between a prey and unwanted clutter (e.g. foliage) by effectively employing their sonar. According to recent studies, big brown bats can discriminate clutter by transmitting two different carrier frequencies. Most interestingly, there are many striking analogies between the characteristics of the bat sonar and the one of a radar sounder. Among the most important ones, they share the same nadir acquisition geometry and transmitted signal type (i.e. linear frequency modulation). In this paper, we explore the feasibility of exploiting frequency diversity for the purpose of clutter discrimination in radar sounding by mimicking unique bats signal processing strategies. Accordingly, we propose a frequency diversity clutter reduction method based on specific mathematical conditions that, if verified, allow the disambiguation between the clutter and the subsurface signal to be performed. These analytic conditions depend on factors such as difference in central carrier frequencies, surface roughness and subsurface material properties. The method performance has been evaluated by different simulations of meaningful acquisition scenarios which confirm its clutter reduction effectiveness.

Paper Details

Date Published: 18 October 2016
PDF: 10 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 1000417 (18 October 2016); doi: 10.1117/12.2241243
Show Author Affiliations
L. Carrer, Univ. degli Studi di Trento (Italy)
L. Bruzzone, Univ. degli Studi di Trento (Italy)


Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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