Novel Method for Radar Echo Target Detection

Radar target detection, as one of the pivotal techniques in radar systems, aims to extract valuable information such as target distance and velocity from the received energy echo signals. However, with the advancements in aviation and electronic information technology, there have been profound transformations in the radar detection targets, scenarios, and environments. The majority of conventional radar target detection methods are primarily based on Constant False Alarm Rate (CFAR) techniques, which rely on certain distribution assumptions. However, when the detection scenarios become intricate or dynamic, the performance of these detectors is significantly influenced. Therefore, ensuring the robust performance of radar target detection models in complex task scenarios has emerged as a crucial concern. In this paper, we propose a radar target detection method based on a hybrid architecture of convolutional neural networks and autoencoder networks. This approach comprises clutter suppression and target detection modules. We conducted ablation experiments and comparative experiments using publicly available radar echo datasets and simulated radar echo datasets. The ablation experiments validated the effectiveness of the clutter suppression module, while the comparative experiments demonstrated the superior performance of our proposed method compared to the alternative approaches in complex background scenarios.