Recognition of high-resolution range profile sequence based on TCN with sequence length-adaptive algorithm and elastic net regularization

Abstract

Radar high-resolution range profile (HRRP) sequences have received great attention in radar automatic target recognition (RATR) due to the advantages of rich target structure features, small storage space requirement, and easy acquisition and processing. The traditional HRRP sequence recognition methods cannot effectively extract the deep time-series features, resulting in poor recognition performance. In addition, the traditional methods are more complex, with weak adaptive capability to different length sequences and robustness to variant targets. To tackle these problems, we propose an efficient method for HRRP sequence recognition based on temporal convolutional networks with sequence length-adaptive algorithm and elastic net regularization (ERTCN-SLA). The proposed method extracts the deep temporal features of HRRP sequences by stacking multiple modified residual blocks of dilated causal convolution. The batch normalization (BN) is adopted to prevent the over-fitting issue in deep networks due to the offset in parameter distribution. Time-series features extracted by the temporal convolutional neural network are fused using adaptive average pooling, which can reduce the adverse effects of local noise, obtain the global pattern of the time-series features, and improve the robustness and generalizability of temporal convolutional networks (TCN). Further, the unstructured sparsity of the model is achieved by using the elastic net regularization method for feature selection. A sequence length-adaptive algorithm based on the Bayesian optimization algorithm is proposed to achieve adaptive end-to-end training and recognition for HRRP sequences of different lengths. Experimental results on moving target and stationary target acquisition and recognition (MSTAR) datasets show that the proposed method has superior recognition accuracy compared with other remarkable time-series methods. Furthermore, the proposed method has better robustness and sparsity.