Robust sequential high‐resolution range profile recognition based on the infinite conditional restricted Boltzmann machine

Gao, Xueshan; Peng, Xuan

doi:10.1049/iet-rsn.2019.0012

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…According to the scale-space theory [21,22], the multiscale representation of HRRP is achieved by uniformly sampling the signal under continuous scale factors. Since the sampling vector of HRRP is a one-dimensional signal, its multi-scale extraction is…”

Section: Multi-scale Features and Dimension Reduction Mechanism Analy...mentioning

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: Multi-scale Features and Dimension Reduction Mechanism Analy...mentioning

confidence: 99%

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

Yang,

Zhang,

Song

2024

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

show abstract

“…Then the joint probability distribution of the model is ( 2 ) where the energy function The entire structure of Dis-RBM is connected only between the hidden cell layer and the visible cell layer, which means that the hidden and visible variables are conditionally independent of each other in their states, i.e.,…”

Section: Given a Training Dataset Containing K-class Targets Data Setmentioning

confidence: 99%

“…HRRP recognition techniques face severe challenges of accuracy and robustness [1,2] . According to the different principles of classifiers, HRRP sequence recognition methods can be roughly divided into three categories.…”

Section: Introductionmentioning

confidence: 99%