A Low Slow Small Target Classification Network Model Based on K-Band Radar Dynamic Multifeature Data Fusion

Micro-Doppler (m-D) signals are susceptible to interference from a large number of Doppler signals and ambient noise, and the single use of m-D signatures (MDSs) for the classification of small, slow, and low-speed targets poses certain limitations. In this article, a dynamic multifeature data fusion neural network (DMFFNNet) classification method is proposed. First, K-band frequency-modulated continuous-wave (FMCW) radar is used to collect echo data from five types of rotor drones and bionic bird. After preprocessing the data, 2-D range-period graphic and 2-D time-frequency (TF) spectrograms are obtained. We investigate the construction of new data representations in the range-periodic domain, designing networks to extract dynamic time-varying features of the data. To be able to obtain accurate localized features, a local feature extraction module is proposed to extract local features from the range-period graph, while a global feature extraction module is used to extract global features from the TF spectrograms. To be able to extract dynamic information about the data, a 3-D network is used to capture dynamic change feature in the 3-D range-period data. Finally, a feature fusion module is designed to integrate the extracted features, and to be able to better extract the features of the target, an attention mechanism is added to the fusion network to extract the temporal and spatial features in the spectrogram and fuse them to further improve the overall performance of the model. Experimental results show that compared with single-channel CNN classification methods, incorporating dynamic feature data enables the network to achieve better classification accuracy.