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- W2360736309 abstract "The non-linear manifold structure property of inverse synthetic aperture radar(ISAR)images is analysed intensively,and it is pointed that the ISAR images can be viewed as a non-linear manifold of high-dimensional ISAR image space controlled by a few parameters,such as position,attitude and scale.The idea of non-linear manifold learning is introduced into ISAR target recognition,a new feature extraction and recognition method for 2-D ISAR images based on Locality Preserving Projections(LPP)algorithm and k-nearest neighbor classification is proposed.Firstly,the LPP algorithm is used to reduce the dimensionality of the ISAR images,and then four kinds of aircraft target are classified by k-nearest neighbor classification with rejection capability in the low-dimensional subspace.The simulated experiment results suggest that the LPP algorithm has the capability of finding the low-dimensional manifold structure embedded in the high-dimensional ISAR image space,and a higher recognition rate is acquired with the low-dimensional feature obtained by LPP." @default.
- W2360736309 created "2016-06-24" @default.
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- W2360736309 date "2010-01-01" @default.
- W2360736309 modified "2023-09-28" @default.
- W2360736309 title "ISAR Target Recognition Based on Non-linear Manifold Learning" @default.
- W2360736309 hasPublicationYear "2010" @default.
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