With the capability of capturing a target’s two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target’s attitude can lead to the scatterers’ rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method.

Existing methods for inverse synthetic aperture radar (ISAR) target recognition typically rely on a single high-resolution radar signal type, such as ISAR images or high-resolution range profiles (HRRPs). However, ISAR images and HRRP data offer representations of targets across different aspects, each containing valuable information crucial for radar target recognition. Moreover, the process of generating ISAR images inherently facilitates the acquisition of HRRP data, ensuring timely data collection. Therefore, to fully leverage the different information from both HRRP data and ISAR images and enhance ISAR ship target recognition performance, we propose a novel deep fusion network named the Separation-Decision Recognition network (SDRnet). First, our approach employs a convolutional neural network (CNN) to extract initial feature vectors from ISAR images and HRRP data. Subsequently, a feature separation module is employed to derive a more robust target representation. Finally, we introduce a weighted decision module to enhance overall predictive performance. We validate our method using simulated and measured data containing ten categories of ship targets. The experimental results confirm the effectiveness of our approach in improving ISAR ship target recognition.

Most existing target recognition methods based on high-resolution range profiles (HRRPs) use data from only one domain. However, the information contained in HRRP data from different domains is not exactly the same. Therefore, in the context of inverse synthetic aperture radar (ISAR), this paper proposes an advanced deep hybrid fusion network to utilize HRRP data from different domains for ship target recognition. First, the proposed network simultaneously processes time-domain HRRP and its corresponding time–frequency (TF) spectrogram through two branches to obtain initial features from the two HRRP domains. Next, a feature alignment module is used to make the fused features more discriminative regarding the target. Finally, a decision fusion module is designed to further improve the model’s prediction performance. We evaluated our approach using both simulated and measured data, encompassing ten different ship target types. Our experimental results on the simulated and measured datasets showed an improvement in recognition accuracy of at least 4.22% and 2.82%, respectively, compared to using single-domain data.