Radar target shape recognition using a gated recurrent unit based on RCS time series' statistical features by sliding window segmentation

Abstract

The radar cross section (RCS) is an important parameter that reflects the scattering characteristics of radar targets. Based on the monostatic radar RCS time series' statistical features by sliding window segmentation, a novel sliding window-statistical-gated recurrent unit (SW-S-GRU) method for radar target recognition (RTR) is proposed using a GRU. The sliding window method divides the RCS time series into many segments for obtaining the statistical features as the input of the GRU. The deeper features are extracted from the segmented time series and then classified by the GRU. Under the model of precession motion, simulation results show that the proposed method achieves a great recognition accuracy. Even under the condition of a low SNR, the proposed method can obtain better recognition accuracy. For the precession dataset, the effect of sliding window parameters on RTR is analysed. The simulation results show that the recognition result of the model is better using the longer series overlap length between adjacent times. And when the number of hidden layers is 8 or above 8, the recognition accuracy does not change. Using the publicly available measured drone RCS data, the proposed method can also identify the drone well.