Prognosis of the Effects of Soil Characteristics on the Performance of Landmine Detection in Ground-Penetrating Radar System - A Case Study
Publish Year: 1387
نوع سند: مقاله کنفرانسی
زبان: English
View: 2,245
This Paper With 10 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ICTINDT02_037
تاریخ نمایه سازی: 3 شهریور 1387
Abstract:
The landmines presence causes serious safety hazards, which demand the clean up of contaminated land. Ground Penetrating Radar (GPR) is considered as a powerful nondestructive testing tool for high-resolution imaging of the shallow subsurface and its ability to detect both metallic and non-metallic landmines. The contrast in the dielectric constant between a landmine and the surrounding soil is one of the most important parameters to be considered when using GPR for landmine detection. In this paper, we discuss available models for the prediction of the dielectric constant from soil physical properties including bulk density, particles density soil texture, and water content. We predict the effects of such properties on the landmine detection performance of GPR in Iran. At first, we use available soil geophysical information from four soils selected among Iranian areas. Afterward, semi-empirical model was developed to predict whether or not field conditions are appropriate for use of GPR implements. The predictions of this model were in different soil textures at various soil water contents. Knowledge of soil texture and soil water content variability and how these affect soil electrical properties of the soil is essential for the effective development and deployment of GPR systems for landmine clearance operations. The model presented here can be useful for making this determination.
Keywords:
Authors
Mohammad Riahi
Associate Professor
Amirhossein Tavangar
Ph.d. Graduate Student, Ryerson University, Toronto, Canada
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :