Except for the slight difference of micromotion parameters, some decoys and warheads have the same geometry and micromotion form. As a result, recognition of similar space cone–cylinder targets is one of the difficult problems in ballistic target recognition. In recent years, due to the good effect of deep neural networks (DNNs) in optical target recognition, space cone–cylinder target recognition methods based on DNN have attracted wide attention. However, these DNN-based methods only recognize the space cone–cylinder targets with different shapes and different micromotion forms. Moreover, these methods require some time-consuming preprocessing operations, which need to observe the target for at least one micromotion period. To recognize similar space cone–cylinder targets, we propose a complex-valued coordinate attention networks (CV-CANets)-based end-to-end recognition method. Firstly, we establish the signal model of space cone–cylinder targets. Secondly, we propose CV-CA blocks by transforming the coordinate attention mechanism into the complex-valued domain. Then, we construct CV-CANet based on the proposed CV-CA blocks. Finally, the proposed CV-CANet is trained and tested by the narrowband radar echo data, which is generated by electromagnetic calculation. Compared with the convolutional neural network (CNN)-based recognition methods, the proposed method can not only recognize the similar space cone–cylinder targets but also is superior in terms of time cost and observation requirement. Extensive experiments validate that the proposed recognition method is effective when the targets only have a slight difference on the precession angular frequency and the observation time is less than half a period.

High-resolution range profile (HRRP) sequences have great potential for space target classification because they can provide both scattering information and micro-motion information. However, many factors cause an obtained HRRP sequence for a space target to be corrupted in real cases due to noise interference, limited radar resources, and the requirement of multi-target observations. Many space target classification methods cease to be effective when HRRP sequences are corrupted, so classifying space targets with corrupted HRRP sequences is still a challenging problem. To solve this problem, a novel space target classification method based on a temporal-spatial feature aggregation network (TSFA-Net) is proposed by using the corrupted HRRP sequences directly. First, a sequence-to-token module (S2T-module) is designed to extract low-level and fine-grained features from the raw inputs. Second, to effectively model the long-range dependencies among corrupted HRRP sequences and capture global representations without losing target local features, we propose a parallel and dual-branch block, i.e., a temporal-spatial feature aggregation block (TSFA-block), by combining a Transformer network and a convolutional neural network (CNN). Then, via progressively hierarchically stacking TSFA blocks, a hierarchical temporal-spatial feature aggregation subnetwork (H-TSFA-subnetwork) is constructed to obtain the final temporal-spatial features. Finally, a token-to-label module (T2L-module) is adopted to obtain the classification results. Extensive experiments demonstrate that the proposed method achieves state-of-the-art classification accuracy for space target classification with HRRP sequences, especially under the conditions of a low signal-to-noise ratio and a high missing rate.

Range instantaneous Doppler (RID) methods show superior advantages in imaging performance over range-Doppler (RD) algorithm for maneuvering targets. However, traditional RID methods are susceptible to noise and sparse aperture (SA), which makes the resolution of imaging results low and the quality degraded. To improve the imaging performance, we propose a RID high-resolution (HR) imaging framework for maneuvering targets, in which a Hybrid Transformer-based HR network (HTHRNet) is introduced into the RID method. HTHRNet is trained to map low-resolution (LR) time-frequency distribution (TFD) images to HR TFD images under different conditions. Our deep neural network fuses the convolutional neural network (CNN) and Transformer to make full use of their advantages. In addition, we modify the network structure to make HTHRNet training easier and more efficient. The experimental results of TFD data, simulated data, microwave chamber data and measured data show that the proposed imaging method has superior performance, which further indicates that the complex deep neural network can be well applied to ISAR HR imaging.

This letter proposes a novel open set domain adaptation method for heterogeneous synthetic aperture radar (SAR) image classification. In this problem, the test data often differ from the distribution of training data and may contain unseen classes that are not present in the training data. Considering the inaccuracy of existing studies in classifying samples of unknown class in target domain, we propose a dynamic self-training network structure to distinguish known classes from unknown class by dynamically adjusting the discriminant threshold. Then in order to further improve the accuracy, a self-training domain-specific weak alignment module is designed, which abandons the previous idea of hard aligning source and target features, and achieves the weak alignment of target domain by the idea of prototype normalization and domain-specific knowledge extraction. Finally, experiments on two heterogenous SAR image benchmark datasets show that the proposed method improves the classification accuracy by more than 3% over the state-of-the-art methods.

For space targets with the same shapes and composite relationships in terms of micromotion forms (STSSCRMFs), the accuracy of deep learning approaches with single-domain data is seriously degraded. To address this issue, this article proposes a hybrid neural network based on feature-level fusion that utilizes multidomain radar information to recognize STSSCRMFs. The proposed network simultaneously processes radar cross-section (RCS) time series, high-resolution range profile (HRRP) sequences, and time-frequency (TF) spectrograms through three branches, leveraging their complementary characteristics. Additionally, an attention mechanism is incorporated into the feature-level fusion module to selectively enhance the features of important domains, thereby achieving improved space target recognition. Consequently, the temporal-spatial features of multidomain data are adaptively fused, capturing discriminative and complementary representa-tions, which effectively enhances the accuracy of space target recognition. Experimental results demonstrate the superiority and effectiveness of the proposed method.

The micro-Doppler effect of radar targets reflects the target motion attribute, which can provide important information for target recognition. Based on the micro-Doppler effect, the estimation of micro-motion parameters of the cone is of great significance in the field of anti-missile. However, the existing methods based on monostatic radar can only extract some micro-motion parameters, and the research on micro-Doppler properties is not in-depth enough. In the article, the micro-Doppler properties of the precession cone with fins are studied. Firstly, the motion model of the precession cone with fins is given. Subsequently, the frequency shift equation of the fin is studied, and a set of equations containing the relationship between the micro-motion parameters is constructed. The micro-motion parameters (precession angle, bottom radius, angle between conning vector and radar line of sight) are estimated by solving the set of equations. The effectiveness of the algorithm is verified by simulated data and measured data.