Geometrical Structure Classification of Target HRRP Scattering Centers Based on Dual Polarimetric $H/\alpha$  Features

Li, Teng; Zhang, Liang; Li, Yang; Wang, Yanhua

doi:10.1109/access.2019.2942425

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…First, the polarized scattering components can be represented as a linear combination of eigenvalues and eigenvectors [ 1 , 5 , 11 ]. The decomposition into eigenvectors and eigenvalues for the Mueller matrix can be an efficient approach to polarimetric recognition [ 34 , 35 ]. The recognition approach is to use the coherence matrix (Equation (2)) as the

polarimetric decomposition [ 11 , 14 ].…”

Section: Polarimetric Decomposition Of Feature Spacementioning

confidence: 99%

Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition

Kvasnov

2023

Sensors

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This article deals with the analysis of backscattering at automatic target recognition (ATR) by ground-based radar located on rough terrain surfaces, using the properties of wave polarization. The purpose of the study is to examine and compare linear and circular polarized reflected waves, which can be described by decomposition theorems. Coherent decompositions (Pauli, Krogager, Cameron decomposition) are considered in the case of a rough terrain, for which the advantage of the Pauli decomposition has been shown. The article demonstrates an approach to the extraction of polarization signal backscattering data for two types of vehicles with different profiles. It is shown that the measurement results can be calibrated by a corner reflector that takes into account the properties of the ground surface, and further used for ATR based on supervised learning algorithms. The accuracy of object classification was 68.1% and 54.2% for the signal generated by linearly and elliptically polarized waves, respectively. Based on these results, we recommend using a linearly polarized wave as an object recognition mechanism. At the same time, any reflected depolarized wave significantly reshapes the structure due to the rotation of the object profile and the influence of a rough surface (vegetation fluctuations). This explains the low recognition accuracy in general.

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polarimetric decomposition [ 11 , 14 ].…”

Section: Polarimetric Decomposition Of Feature Spacementioning

confidence: 99%

Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition

Kvasnov

2023

Sensors

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“…For a data-driven radar HRRP target recognition task, which is also regarded as a cooperative target recognition, 1,5,6,10,11,15,17,[20][21][22][31][32][33][34][35][36][37][38] we are provided with an independently and identically distributed set of HRRP samples X…”

Section: Hrrp-based Target Recognitionmentioning

confidence: 99%

“…For a data‐driven radar HRRP target recognition task, which is also regarded as a cooperative target recognition, 1,5,6,10,11,15,17,20‐22,31‐38 we are provided with an independently and identically distributed set of HRRP samples

X = {x_{1}, x_{2}, …, x_{N}}

x_{i} \in R^{D}

with corresponding true labels

Y = {y_{1}, y_{2}, …, y_{N}}

y_{i} \in {1,2, …, S}

from different azimuths,

N

and

S

refers to the number of samples and classes, respectively. The deep learning method we used, a.k.a., CNN, is a kind of supervised learning and consists of two main stages, namely, the training stage and the evaluating stage.…”

Section: Problem Formulationmentioning

confidence: 99%

One‐dimensional convolutional neural networks for high‐resolution range profile recognition via adaptively feature recalibrating and automatically channel pruning

et al. 2020

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High-resolution range profile (HRRP) has obtained intensive attention in radar target recognition and convolutional neural networks (CNNs) are among predominant approaches to deal with HRRP recognition problems. However, most CNNs are designed by the rule-of-thumb and suffer from much more computational complexity. Aiming at enhancing the channels of one-dimensional CNN (1D-CNN) for extracting efficient structural information oftargets form HRRP and reducing the computation complexity, we propose a novel framework for HRRP-based target recognition based on 1D-CNN with channel attention and channel pruning. By introducing an aggregationperception-recalibration (APR) block for channel attention to the 1D-CNN backbone, channels in each 1D convolutional layer can adaptively learn to recalibrate the extracted features for enhancing the structural information captured from HRRP. To avoid rule-ofthumb design and reduce the computation complexity

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“…Transforming features describe the property of HRRP in a transformation domain, such as the spectra and the micro-Doppler [37,38]. Various classifiers can be used for target classification, such as decision tree, support vector machine (SVM) [39], Bayes classifier [40][41][42] and ensemble learning. A decision tree [43] is a flowchart-like predictive model whose internal nodes represent individual decision rules.…”

Section: A Radar Target Hrrp Recognitionmentioning

confidence: 99%

Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network

Song

Wang

et al. 2020

IEEE Access

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In radar high-resolution range profile (HRRP) recognition, the recognition accuracy will decline when the training samples in some classes (majority classes) greatly outnumbers other classes (minority classes). To alleviate the above imbalanced problem, an HRRP data augmentation framework is proposed. A onedimensional (1-D) deep convolutional generative adversarial network (DCGAN) is developed to generate artificial HRRPs. The fidelity of the generated HRRPs is evaluated subjectively in the raw data domain and quantitatively by the similarity in the feature domain. The experimental results show that the generated data are similar to the true HRRPs and demonstrate that the proposed framework outperforms the state-of-the-art oversampling methods when handling the imbalanced problem. INDEX TERMS High resolution range profile (HRRP), imbalanced problem, data augmentation, 1-D deep convolutional generative adversarial network (DCGAN)

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Geometrical Structure Classification of Target HRRP Scattering Centers Based on Dual Polarimetric $H/\alpha$ Features

Cited by 12 publications

References 36 publications

Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition

Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition

One‐dimensional convolutional neural networks for high‐resolution range profile recognition via adaptively feature recalibrating and automatically channel pruning

Data Augmentation for Imbalanced HRRP Recognition Using Deep Convolutional Generative Adversarial Network

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