Paper
4 September 1998 Prediction of buried minelike target radar signatures using wideband electomagnetic modeling
Abbie L. Warrick, Stephen G. Azevedo, Jeffrey E. Mast
Author Affiliations +
Abstract
Current ground penetrating radars (GPR) have been tested for land mine detection, but they have generally been costly and have poor performance. Comprehensive modeling and experimentation must be done to predict the electromagnetic (EM) signatures of mines to access the effect of clutter on the EM signature of the mine, and to understand the merit and limitations of using radar for various mine detection scenarios. This modeling can provide a basis for advanced radar design and detection techniques leading to superior performance. Lawrence Livermore National Laboratory (LLNL) has developed a radar technology that when combined with comprehensive modeling and detection methodologies could be the basis of an advanced mine detection system. Micropower Impulse Radar (MIR) technology exhibits a combination of properties, including wideband operation, extremely low power consumption, extremely small size and low cost, array configurability, and noise encoded pulse generation. LLNL is in the process of developing an 'optimal' processing algorithm to use with the MIR sensor. In this paper, we use classical numerical models to obtain the signature of mine-like targets and examine the effect of surface roughness on the reconstructed signals. These results are then qualitatively compared to experimental data.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Abbie L. Warrick, Stephen G. Azevedo, and Jeffrey E. Mast "Prediction of buried minelike target radar signatures using wideband electomagnetic modeling", Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); https://doi.org/10.1117/12.324154
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Land mines

Mining

Radar

Signal processing

Algorithm development

Surface roughness

Detection and tracking algorithms

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