在雷达HRRP识别中多特征融合多类分类器设计

doi:10.3969/j.issn.1001-2400.2013.01.020

Abstract

Abstract:

A multi-class classifier for feature fusion is designed based on the traditional linear Relevance Vector Machine (RVM). The proposed classifier extends the binary RVM to multi-class RVM based on the multinomial Probit regression model, utilizes the feature selection property of the linear RVM to reduce the dimensionality of the fused feature vector, and makes the linear RVM framework have the ability to form the nonlinear classification boundary via the rational power extension. All of these properties can ensure the robust fusion recognition performance in the case of nonlinear multi-class classification. For the application of radar target recognition using the high-resolution range profile (HRRP), the experimental results on the measured data show that the proposed multi-class classifier with the three translation-invariant features extracted from HRRP data can achieve robust recognition performance.

Key words: relevance vector machine, multi-class classifier, feature fusion, feature selection, high-resolution range profile, radar target recognition

LI Zhipeng;MA Tianxiang;DU Lan;XU Danlei;LIU Hongwei;ZHANG Zijing. Multi-class classifier design for feature fusion in radar HRRP recognition[J].J4, 2013, 40(1): 111-117.

References

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Multi-class classifier design for feature fusion in radar HRRP recognition

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