Polarimetric high resolution range profile (HRRP) holds great potential for radar automatic target recognition (RATR) owing to its capability of providing both polarimetric and spatial scattering information. Conventional polarimetric HRRP recognition methods generally extract a set of features and put them into a well-trained classifier, which heavily rely on expertise. On the contrary, deep learning can automatically learn deep features of the training data and has obtained state-of-the-art results in many classification tasks. In this paper, a novel deep model based on convolutional long short-term memory (ConvLSTM) network and self-attention mechanism is proposed for polarimetric HRRP recognition. In the proposed model, ConvLSTM employs the convolutional operator and recurrent LSTM structure to capture and aggregate target polarimetric and spatial scattering information from different dimensions simultaneously. Self-attention mechanism is introduced before ConvLSTM to make the model focus on the discriminative range cells and help improve the learning capacity of ConvLSTM. Experiment results on the simulated and measured datasets demonstrate the effectiveness of the proposed model for multi-class targets recognition. The results of two expanded experiments also show that this model performs well in noise and small training sample cases, showcasing good potentials in practical applications.

High-resolution range profile (HRRP) is increasingly employed in radar target recognition under intricate ground scenarios. Such scenarios demand recognizing the specific type of a target from a wide range of categories, a task known as fine-grained target recognition (FGTR), which involves numerous and potentially unbalanced categories. To tackle this, we propose a joint semantic-data guided hierarchical classification (SDHC) framework. It consists of a set of local classifiers organized in a tree hierarchy based on the joint semantic-data relationship. It allows the complex FGTR task to be simplified into multiple small-scale sub-tasks. Specifically, the proposed SDHC method focuses on tree hierarchy construction and local classifier training. We design the tree hierarchy based on a joint semantic-data similarity measure, which quantifies the data similarity between categories and incorporates semantic knowledge constraints. Following this, we deploy hierarchical feature selection on a multi-dimensional feature set, considering the contribution of features in each local classifier. Experimental results on measured data verify the effectiveness of the proposed method. Moreover, analysis results demonstrate the superiority of the hierarchical approach over flat methods.

High resolution range profile (HRRP) is essential for high-throughput radar automatic target recognition. Due to the aspect sensitivity of HRRP, the recognition performance degrades when the training dataset misses some aspects. Inspired by disentangled representation learning (DRL), this letter proposes a type-aspect disentanglement network to alleviate the above aspects missing problem. The proposed method disentangles HRRP into uncorrelated type and aspect representations, so that the type representation can be used to accurately recognize target without being affected by target aspects. Specifically, the proposed network acquires initial type and aspect representations by residual factorization. On this basis, mutual information (MI) minimization is used to constrain the independence of two representations from each other, and a reconstruction loss is employed to avoid information reduction during the decorrelation process. Additionally, target type and aspect labels are exploited to constrain their semantic meanings. Experimental results demonstrate that the proposed method achieves improved robustness in varying degrees of aspects missing cases.

High resolution range profile (HRRP) plays a crucial role in radar target recognition. In real-world applications, variations in operational conditions during testing, such as changes in depression angles, result in unsatisfactory performance for HRRP target recognition methods. One way to alleviate this issue is to augment training data with samples that embody the testing domain style. Therefore, we propose a domain-adaptive HRRP generation approach based on a two-stage denoising diffusion probability model (DDPM). In the first stage, we leverage the category labels as conditioning factors, ensuring precise category control over the pre-generated contents. In the second stage, we harness style information from reference samples to steer the pre-generated content closer to the testing domain. By augmenting training data with the generated samples, the disparity between two domains is bridged. Results on the moving and stationary target acquisition and recognition (MSTAR) dataset show that the proposed method improves the recognition rate by 2.01% and 4.93% for the data of 15° and 17° depression angle, respectively.

Polarimetric high resolution range profile (HRRP) contains the geometrical structural information along radar line-of-sight and has shown great potentials in target recognition. In existing researches, H/α decomposition has been applied to exploit global polarimetric features of a single HRRP from its spatially averaged coherent matrix, which loses target local scattering information. In this paper, we propose to apply the H/α decomposition along the slow time dimension in a dual polarimetric HRRP sequence. The H and α features are extracted for each target scattering center by averaging its samples in different observations of the HRRP sequence, which provides both target local high resolution and polarimetric scattering characteristics. We also design a novel six-zone H-α plane for geometrical structure classification of target scattering centers. Simulation and experimental results show the effectiveness of the proposed method and display its good potentials for practical applications.