Novel multi-dimensional feature radar target detection method with deep forest

Accurate target detection under heavy background noise and clutter remains the key challenge for radar applications. Traditional target detection methods based on statistical models have limited performance in complex environment. Although advanced deep learning methods such as Convolutional Neural Networks (CNN) have been introduced to suppress clutter and improve the target detection performance, the limited interpretability and flexibility hinders their further implication and extension. To address this challenge, A novel multi-dimensional feature target detection method based on deep forest (DF) is proposed in this paper. Firstly, a low threshold Constant False Alarm Rate (CFAR) detector is employed to preprocess the received radar signals. Afterwards, the multi-dimensional features of filtered signals are classified using the designed DF model, which could deeply express the difference between radar targets and clutter. Finally, comparison has been conducted against CFAR and CNN by using experimental data and the results indicate that the proposed method significantly improves radar target detection capability under complex environment.