A statistical model of the area cleared by a landmine removal vehicle using real-time kinematic differential GPS and inertial sensing technologies

Graduation Date

2004

Document Type

Thesis

Program

Other

Program

Thesis (M.S.)--Humboldt State University, Master of Science In Environmental Systems: Mathematical Modeling, 2004.

Committee Chair Name

Kenneth D. Owens

Committee Chair Affiliation

HSU Faculty or Staff

Keywords

Humboldt State University -- Theses -- Mathematical Modeling, Inertial sensing technologies, Landmine clearance, Landmine removal vehicles, Global Positioning System

Abstract

Landmines are currently being cleared using unmanned vehicles. There is a need for verification of the ground covered by a vehicle's landmine clearing apparatus to ensure clearance of the entire minefield. Recent advancements in satellite positioning technology now make accurate verification possible. In this project we will model the ground covered by the clearance apparatus of a landmine clearance vehicle to a specified confidence level using real-time kinematic differential GPS and inertial sensing technologies. A two-dimensional probability density function of the ground covered by the front flail will be developed. The probability density function is intended to be a main component in a real-time two-dimensional mapping application for tracking the ground covered by a landmine clearance vehicle. It will be a function of the following: • Flail size (length and width) • Position of the flail in relation to the GPS antenna • GPS antenna position • GPS antenna position error • Vehicle heading • Vehicle heading error This function relates to the probability a point was covered when given the estimated vehicle state by an INS/GPS system. A method for implementing the probability density function into software is explained. The viability of the software implementation is then tested through an experiment conducted using a prototype landmine clearance vehicle. Results of the experiment show that the implementation is effective at limiting both Type I and Type II errors.

https://scholarworks.calstate.edu/concern/theses/9w0325172

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