Research Article
A Radar Target Detection Method Based on RBF Neural Network
@INPROCEEDINGS{10.1007/978-3-030-93398-2_66, author={Hu Jurong and Wu Tong and Lu Long and Li Xujie}, title={A Radar Target Detection Method Based on RBF Neural Network}, proceedings={Wireless and Satellite Systems. 12th EAI International Conference, WiSATS 2021, Virtual Event, China, July 31 -- August 2, 2021, Proceedings}, proceedings_a={WISATS}, year={2022}, month={1}, keywords={Radar Constant false alarm rate Neural network Target detection}, doi={10.1007/978-3-030-93398-2_66} }- Hu Jurong
Wu Tong
Lu Long
Li Xujie
Year: 2022
A Radar Target Detection Method Based on RBF Neural Network
WISATS
Springer
DOI: 10.1007/978-3-030-93398-2_66
Abstract
In recent years radar target detection environment is more and more complex. Traditional CFAR (Constant False Alarm Rate) is a technology in which the radar system discriminates the output signal and noise of the receiver to determine whether the target signal exists under the condition that the False Alarm probability is kept Constant. In order to improve the radar target detection performance, a radar target detection method based on NN (Neural Network) is proposed. In this paper, the radar signal received by a single RBFNN is used for network training, and the probability of detection target is studied by combining the binary detection theory. Simulation results show that the proposed algorithm can effectively improve the radar target detection probability.
