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.
KEYWORDS: Image segmentation, Data modeling, Transformers, Target detection, Radar sensor technology, Network architectures, Deep learning, Systems modeling, Radar, Education and training
During the process of radar detection, various factors such as noise and clutter interfere with the received echo signals, leading to the detection of a large number of false targets. This poses significant challenges to the detection, tracking, and identification of real targets. This paper introduces a Transformer-based segmentation network for false target suppression. It innovatively transforms the problem of false target suppression in sequential signals into an image segmentation task. The dice loss is effectively employed to address the issue of class imbalance between targets and backgrounds. Furthermore, the introduction of the transformer module further enhances the segmentation performance of the model. The proposed method is validated on real radar measurement data, demonstrating both the effectiveness of the designed model and the improvements brought by each module.
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