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

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 ...

AI Technical Summary

Problems solved by technology

The recognition effect of this method is better than KLDA under the condition of sufficient training samples, but for small sample conditions such as sea surface ship targets, the recognition effect of the two methods is not ideal

Images