Software Defined Doppler Radar for LandmineDetection using GA-Optimized Machine Learning
Zhang, Yan
Zhang, Yan
Citations
Altmetric:
License
License
DOI
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.
Description
4/16/2020
3:00pm-5:00pm
Graduate
3:00pm-5:00pm
Graduate
Date
2020-01-01