Radar high‐resolution range profiles target recognition based on stable dictionary learning

Liu, Hongwei; Bao, Feng; Chen, Bin; Du, Lan

doi:10.1049/iet-rsn.2015.0007

Cited by 45 publications

(12 citation statements)

References 30 publications

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“…Radar High-resolution range profile (HRRP), is composed of the amplitude of the coherent summations of the complex returns from target scatterers in each range cell, which represents the projection of the complex returned echoes from the target scattering centers onto the radar line-of-sight (LOS). Since HRRP contains abundant target structure signatures, such as target size, scatterer distribution, etc., HRRPbased radar automatic target recognition (RATR) has received intensive attention from the RATR community [1]- [24].…”

Section: Introductionmentioning

confidence: 99%

Recognizing the HRRP by Combining CNN and BiRNN With Attention Mechanism

et al. 2020

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In this paper, we integrate the advantages of convolutional neural network (CNN) and bidirectional recurrent neural network (BiRNN) with attention mechanism, and propose a CNN-BiRNN based method to recognize the individual high resolution range profile (HRRP). In the proposed method, the CNN is utilized to explore the spatial correlation of raw HRRP data and extract expressive features followed by a BiRNN taking the full consideration of temporal dependence between range cells. Furthermore, in order to enhance the robustness to misalignment, an attentional mechanism is employed after BiRNN to allow the CNN-BiRNN model to focus on the discriminative target area. The combination of CNN and BiRNN with attention mechanism makes the extracted features are not only efficient, but also strongly resistant to the time-shift sensitivity. Experimental results on measured HRRP data demonstrate the effectiveness and the robustness to misalignment of the proposed method. INDEX TERMS Radar automatic target recognition (RATR), high-resolution range profile (HRRP), convolutional neural networks (CNNs), recurrent neural networks (RNNs), attention mechanism.

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Section: Introductionmentioning

confidence: 99%

Recognizing the HRRP by Combining CNN and BiRNN With Attention Mechanism

et al. 2020

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“…Liu et al [4] proposed a stable dictionary learning method to solve the problems of mismatched and abnormal amplitudes between the sparse representations of similar targets in HRRP signals. Du et al [5] introduced a factorized discriminative conditional variational autoencoder (FDCVAE) to improve the robustness of aspect angle feature extraction.…”

Section: Introductionmentioning

confidence: 99%

HRRP Target Recognition With Deep Transfer Learning

Wen

Shi

et al. 2020

IEEE Access

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Recently, radar high-resolution range profile (HRRP) recognition based on convolutional neural networks (CNNs) has received considerable attention due to its robustness to translation and amplitude changes. Most of the existing methods require that sufficient labeled data with complete aspect angles be used as training data, which is a difficult task in practice. In addition, HRRP signals have a high sensitivity to the aspect angle. Therefore, the representative and discriminative powers of the features extracted from typical CNN models are reduced due to incomplete aspect angles in the training data, which significantly limit the recognition performance. This paper first considers the problem of HRRP recognition with incomplete aspect angle training data and addresses the problem by a deep transfer learning framework. Specifically, the two proposed methods enhance the recognition performance by exploring the discriminative power and the intraclass consistency with auxiliary data, which have HRRP signals with complete aspect angles. This paper generates a simulated HRRP dataset from public data to validate the proposed work. The comparisons of the recognition results demonstrate that the proposed framework outperforms the latest CNN-based models. INDEX TERMS High-resolution range profile, target recognition, deep learning, transfer learning. YI WEN received the B.S. degree from Xiamen University, Xiamen, China, in 2018, where she is currently pursuing the master's degree with the Department of Communication Engineering, School of Information Science and Engineering. Her main research interests include deep learning and signal processing. LIANGCHAO SHI received the B.S. degree from Xiamen University, Xiamen, China, in 2018, where he is currently pursuing the master's degree with the Department of Communication Engineering, School of Information Science and Engineering. His main research interests include deep learning and signal processing.

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“…It is significant to adopt reasonable and effective features to improve recognition performance. Currently, the targets of HRRP recognition research are basically about ground or aviation targets, such as tanks and airplanes [8,9,10,11,12,13,14,15]. Satellites, as an important space target, have different motion characteristics, one of which is that their motion must follow Kepler’s law.…”

Section: Introductionmentioning

confidence: 99%

“…Although these engineered features could play a part in target recognition, they are dependent on researchers’ experience and techniques. Other than the features of artificial selection, machine learning algorithms have been widely utilized to extract features based on high-dimensional HRRP data [11,12,13,14,15]. Principal component analysis (PCA) is applied to extract the complex HRRPs’ feature subspace within each target-aspect sector in the literature [11].…”

Section: Introductionmentioning

confidence: 99%

“…Principal component analysis (PCA) is applied to extract the complex HRRPs’ feature subspace within each target-aspect sector in the literature [11]. Dictionary learning is adopted to extract the features of HRRP data, which possess high noise-robust and discrimination [12,13]. Manifold learning is employed to reduce the feature dimensions of radar HRRP [14,15].…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning-Based Satellite Target Recognition Method Using Radar Data

Wang

Zhang

et al. 2019

Sensors

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A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition.

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Radar high‐resolution range profiles target recognition based on stable dictionary learning

Cited by 45 publications

References 30 publications

Recognizing the HRRP by Combining CNN and BiRNN With Attention Mechanism

Recognizing the HRRP by Combining CNN and BiRNN With Attention Mechanism

HRRP Target Recognition With Deep Transfer Learning

A Deep Learning-Based Satellite Target Recognition Method Using Radar Data

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