A Multi-Feature Based Machine Learning Approach for Ground-Moving Radar Target Detection
Conference
Ahmed, R, Deng, H. (2022). A Multi-Feature Based Machine Learning Approach for Ground-Moving Radar Target Detection
. 533-534. 10.1109/AP-S/USNC-URSI47032.2022.9886580
Ahmed, R, Deng, H. (2022). A Multi-Feature Based Machine Learning Approach for Ground-Moving Radar Target Detection
. 533-534. 10.1109/AP-S/USNC-URSI47032.2022.9886580
This paper introduces a novel machine learning method for radar ground moving target detection in unknown ground clutters. Several features such as clutter proximity feature, block size, and bending energy are extracted from the radar data in the angle-Doppler domain. They are then applied to a classifier that is trained by target and clutter data for slow-moving radar target detection. The simulation results of the proposed approach validate its effectiveness and demonstrate it is applicable in real ground clutter environments.