Quadruplet depth-wise separable fusion convolution neural network for ballistic target recognition with limited samples

Abstract

Radar high-resolution range profile (HRRP) plays a crucial role in ballistic target recognition due to its simplicity and fast computation. Convolutional neural networks (CNNs), a popular deep learning approach, are widely used in radar automatic target recognition (RATR) based on HRRP, thanks to their excellent feature extraction capabilities. However, current research mainly focuses on integrating branch networks like recurrent neural networks and attention mechanisms into vanilla CNNs to enhance feature extraction. Limited research exists on reducing the computational complexity of vanilla CNNs when applied to HRRP-based RATR, as well as target recognition problems with limited samples. To address these issues, this paper proposes a lightweight depth-wise separable fusion CNN (DSFCNN) for ballistic target HRRP recognition. The DSFCNN reduces the computational complexity of vanilla CNNs while improving recognition accuracy. Furthermore, we introduce the sample-level fitting and class-level distinguishability (SFCD) loss for limited-sample ballistic target HRRP recognition. The SFCD loss ensures better fitting performance of all samples, as well as higher intra-class compactness and inter-class separability, enhancing the distinguishability of embedded features in limited-sample conditions. During the training phase, the SFCD loss is equally applied to four DSFCNNs with shared structures and parameters, resulting in a model known as Quadruplet DSFCNN (QDSFCNN). However, during the testing phase, only one DSFCNN is utilized. Additionally, we propose the parameter quantity shifting-fitting performance (PQS-FP) coordinate system to elucidate the performance disparities between DSFCNN and vanilla CNNs in our experiments. PQS-FP offers inspirational insights into the relations among parameter quantity, fitting performance, and recognition performance. Extensive experimental results demonstrate the robustness and effectiveness of QDSFCNN for ballistic target recognition in various limited-sample scenarios.