Polarimetric HRRP recognition based on feature‐guided Transformer model

Abstract

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. In recent years, deep learning (DL) has obtained state‐of‐the‐art results in many classification tasks and has drawn great attention in the RATR field. However, as one of the most challenging tasks in RATR, small training sample case will restrict the application of DL because its superior performance generally depends on a large number of training samples. A feature‐guided deep model based on Transformer framework is proposed for polarimetric HRRP recognition with limited training samples. In the proposed model, artificial features are introduced to the attention module to guide the model focus on the range cells of HRRP with more target scattering information so as to reduce the dependence of the model on the number of training samples. Several different approaches are also studied to measure the similarity between artificial features and HRRP data to further improve the learning capacity of the model. Experimental results demonstrate that the proposed feature‐guided Transformer model modifying by Cosine similarity measure is able to achieve a better performance for polarimetric HRRP recognition with limited training samples.

Radar-based Human Activity Classification with Cyclostationarity

“…In the future development, multi-dimensional PCA and other automated feature extractions method could better portray cycle frequencies for classification so that the features would not be strongly correlated among them, which means a easier feature selection. Even though the fusion method employed here was not successful in improving the overall accuracy, the authors believe that the exploitation of complex numbers in classification will improve performances both in terms of convergence rate and also in terms of classification performances [15][16][17].…”

Millimeter-wave radar object classification using knowledge-assisted neural network

“…Our research is continuing, and the data in more complex traffic scenarios, e.g., the crowded situation and strong interference conditions, is still being collected and processed. Since some researches show that introducing knowledge into the network can mitigate the network data size dependence ( Zhang L. et al, 2021 ; Huang et al, 2022 ), in future research, based on our expanded dataset, we will conduct further experiments to assess the effect of knowledge injection with the training data size as the main topic. Simultaneously, the practical application value will be further evaluated with the data in more complex traffic scenarios.…”

“…In Zhang et al (2022) , azimuth angle and phase information were extracted from SAR images and served as domain knowledge to improve the performance of SAR vehicle classification. For polarimetric high-resolution range profile classification, a feature-guided network was proposed with state-of-the-art results ( Zhang L. et al, 2021 ). In the driving assistance system, the information obtained by the tracker has been studied to improve object classification accuracy ( Heuel and Rohling, 2011 ).…”

Ship HRRP target recognition against decoy jamming based on CNN‐BiLSTM‐SE model

“…In most previous HRRP target recognition systems, feature extraction from raw data was a crucial step [3][4][5]. Due to the large dimensionality of unprocessed raw HRRP data, it is redundant and indirect to reflect the inherent properties of the target.…”