High-resolution range profile target recognition method for kernel adaptive mean value discriminant analysis

A high-resolution range image and discriminant analysis technology, applied in the field of high-resolution range image target recognition, can solve problems such as unsatisfactory recognition effect

Active Publication Date: 2018-05-01
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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The recognition effect of this method is better than KLDA under the condition of sufficient training samples, but f

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  • High-resolution range profile target recognition method for kernel adaptive mean value discriminant analysis
  • High-resolution range profile target recognition method for kernel adaptive mean value discriminant analysis
  • High-resolution range profile target recognition method for kernel adaptive mean value discriminant analysis

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[0079] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings, so as to make the purpose, technical solution and advantages of the present invention more clear.

[0080] The invention proposes a high-resolution range image target recognition method based on kernel self-adaptive mean discriminant analysis to improve the target recognition performance in the case of small samples. Aiming at whether the training sample set is sufficient, the kernel adaptive mean discriminant analysis is based on the kernel linear discriminant analysis, and the mean adjustment parameter is introduced to adaptively fuse the local and global information of the samples to obtain the best projection direction. The verification experiment is carried out on the HRRP measured data of sea ships under the condition of small samples. The results show that the features extracted by the kernel adaptive mean discriminant analysis method ha...

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Abstract

The invention discloses a high-resolution range profile target recognition method for kernel adaptive mean value discriminant analysis. The method comprises the steps that an original HRRP signal training set is acquired, l<2> norm normalization is performed to extract power spectrum features, and a feature sample set after preprocessing is obtained; a kernel function is adopted to perform mappingto a high-dimensional feature space; an adaptive dispersion matrix is configured; an optimal projection direction is solved; a new non-linear dimension reduction training feature set is obtained; anSVM classifier is trained; and SVM classification recognition is performed on a to-be-tested original HRRP signal. Through the method, global information of training samples is utilized in a kernel mapping space, local information is adaptively fused during information extraction, low-dimensional features with higher separability compared with common feature extraction and data dimension reductionmethods can be obtained, and recognition precision is improved. The method is also suitable for feature extraction and classification of other signals, such as classification of crack types and sizesthrough a magnetic flux leakage signal in nondestructive testing and audio signal classification.

Description

technical field [0001] The invention relates to a radar target recognition technology, in particular to a high-resolution range image target recognition method based on kernel self-adaptive mean value discriminant analysis. Background technique [0002] Radar high-resolution automatic target recognition (RATR) can be divided into the following three categories according to the spatial dimension: target recognition based on high-resolution range profile (HRRP) samples, two-dimensional imaging (SAR image and ISAR image) target recognition and three-dimensional imaging target recognition . Among them, because HRRP is a one-dimensional vector, it has the characteristics of low computational complexity, fast operation speed, and low occupancy rate of data storage resources. HRRP occupies less resources in computational complexity and data space storage, and HRRP can accurately reflect the target itself. Physical structure information and its distribution of scattering points at ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/02G06F2218/12G06F18/2411G06F18/214
Inventor 袁家雯刘文波朱海霞陈旺才
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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