Orthogonal maximum margin projection subspace for radar target HRRP recognition

Zhou, Daiying

doi:10.1186/s13638-016-0571-y

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…In addition, some classes of ship targets are relatively similar whether in the main structure of the targets or the distribution of scattering points. To solve this problem, the maximum margin criterion (MMC) is an effective method for maximizing the class separability [19,20].…”

Section: Introductionmentioning

confidence: 99%

Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method

Yang,

Zhang,

Song

2024

J. of Syst. Eng. Electron.

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In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection (MSFKSPP) based on the maximum margin criterion (MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile (HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

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Section: Introductionmentioning

confidence: 99%

Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method

Yang,

Zhang,

Song

2024

J. of Syst. Eng. Electron.

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“…A high-resolution range profile (HRRP) is the amplitude of the coherent summations of the complex time returns from target scatters in each range cell, which represents the projection of the complex returned echoes from the target scattering centers on to the radar line-of-sight (LOS) [ 1 ]. The HRRP recognition has been studied for decades in the field of RATR because it contains important structural information such as the target size and the distribution of scattering points [ 1 , 2 , 3 , 4 ]. In addition, the HRRP is easy to obtain, store, and process.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition

Zhang

Gao

Peng

et al. 2018

Sensors

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The High Resolution Range Profile (HRRP) recognition has attracted great concern in the field of Radar Automatic Target Recognition (RATR). However, traditional HRRP recognition methods failed to model high dimensional sequential data efficiently and have a poor anti-noise ability. To deal with these problems, a novel stochastic neural network model named Attention-based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed in this paper. RTRBM is utilized to extract discriminative features and the attention mechanism is adopted to select major features. RTRBM is efficient to model high dimensional HRRP sequences because it can extract the information of temporal and spatial correlation between adjacent HRRPs. The attention mechanism is used in sequential data recognition tasks including machine translation and relation classification, which makes the model pay more attention to the major features of recognition. Therefore, the combination of RTRBM and the attention mechanism makes our model effective for extracting more internal related features and choose the important parts of the extracted features. Additionally, the model performs well with the noise corrupted HRRP data. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that our proposed model outperforms other traditional methods, which indicates that ARTRBM extracts, selects, and utilizes the correlation information between adjacent HRRPs effectively and is suitable for high dimensional data or noise corrupted data.

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“…Introduction: A radar high resolution range profile (HRRP), a real vector, is the amplitude of coherent summations of the complex time return from target scatterers in each range resolution cell. Since it contains abundant target information, many researchers [1][2][3] exploit HRRP for radar automatic target recognition (RATR) in recent years.…”

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confidence: 99%

Radar HRRP recognition based on discriminant deep autoencoders with small training data size

et al. 2016

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A novel radar high resolution range profile (HRRP) recognition method based on discriminant deep autoencoders is proposed to enhance the classification performance with limited training samples. Compared with the conventional models, the proposed method not only extracts high-level feature which can reflect physical structure of HRRP, but also trains HRRP samples globally to reduce the requirement of the training data. The experiment based on the measured data demonstrates the physical meanings of the extracted feature. Moreover, the recognition performance of the proposed method consistently outperforms the conventional models, and the improvement become more significant with smaller training data size.

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Orthogonal maximum margin projection subspace for radar target HRRP recognition

Cited by 11 publications

References 34 publications

Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method

Ship Recognition Based on HRRP via Multi-Scale Sparse Preserving Method

Attention-Based Recurrent Temporal Restricted Boltzmann Machine for Radar High Resolution Range Profile Sequence Recognition

Radar HRRP recognition based on discriminant deep autoencoders with small training data size

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