Multi-Subband Radar Signal Fusion Processing Based on Deep Neural Network in Low Signal-to-Noise Ratio

The traditional multi-subband radar signal fusion method based on pole information fits nonlinear signals via linear models. It is very important to accurately estimate the order of the pole model, as the wrong order will lead to errors in the resulting fusion signal. In the case of low signal-to-noise ratio (SNR), it is difficult to obtain accurate pole values. When a noise suppression method is included in the algorithm, the influence of noise on the pole order may be effectively avoided. However, traditional methods have many complex links, and only a few samples can be tested each time. Also, approximating a linear model onto a nonlinear signal will inevitably have errors. Therefore, this paper proposes a method based on the deep neural network (DNN) that applies nonlinear fitting of deep learning to complete the fusion process. The multi-subband distance envelopes are input into the DNN, and the fusion full-band distance envelopes are obtained as the output. Using DNN for subband fusion could improve the radar range resolution and obtain high-resolution one-dimensional range profiles.