Software Defined Doppler Radar for LandmineDetection using GA-Optimized Machine Learning
Conference Year
January 2020
Abstract
Using software defined radar (SDR) technology and modern machine learning algorithms, this paper demonstrates a software defined Doppler Radar(SDDRadar) system that can distinguish buried non-metallic landmine from other buried objects (rock, wood, etc.) with a high accuracy. In the sensing, the spectrum responses of different buried objects are collected using the SDDRadar. The vibration spectrum data are fed into a Random Forest where a Genetic Algorithm(GA) is designed to optimize the performance of the Random Forest. To leverage SDDRadar sensitivity, a clutter cancellation circuit is designed and integrated into the system. Two outdoor tests are performed under dry and wet soil conditions. For performance evaluation, the GA-optimized Random Forest is compared with other two machine learning algorithms, including Support Vector Machine and Logistic Regression. As it turns out, the GA-optimized Random Forest has the best classification performance in terms of both precision and recall parameters.
Primary Faculty Mentor Name
Tian Xia
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Electrical Engineering
Primary Research Category
Engineering & Physical Sciences
Software Defined Doppler Radar for LandmineDetection using GA-Optimized Machine Learning
Using software defined radar (SDR) technology and modern machine learning algorithms, this paper demonstrates a software defined Doppler Radar(SDDRadar) system that can distinguish buried non-metallic landmine from other buried objects (rock, wood, etc.) with a high accuracy. In the sensing, the spectrum responses of different buried objects are collected using the SDDRadar. The vibration spectrum data are fed into a Random Forest where a Genetic Algorithm(GA) is designed to optimize the performance of the Random Forest. To leverage SDDRadar sensitivity, a clutter cancellation circuit is designed and integrated into the system. Two outdoor tests are performed under dry and wet soil conditions. For performance evaluation, the GA-optimized Random Forest is compared with other two machine learning algorithms, including Support Vector Machine and Logistic Regression. As it turns out, the GA-optimized Random Forest has the best classification performance in terms of both precision and recall parameters.