矩形积分双谱和半监督鉴别分析下的通信辐射源识别

doi:10.12066/j.issn.1007-2861.1991

Abstract

Abstract:

This paper is an attempt towards coping with the problem that the methods of traditional communication emitter identification with the same model performs poorly with extracting fingerprint feature and acquiring high accuracy recognition when the priori label information is insignificant. Targeting the same manufacturer, same batch and same type communication emitter identification of small fingerprint feature difference, an efficient algorithm based on square integral bispectra and semi-supervised discriminant analysis is proposed for communication emitter identification. This algorithm uses square integral bispectra for the extraction of the communication emitter signal bispectra feature as the fingerprint feature, which represents the communication emitter. Simultaneously, for the purpose of improving communication emitter identification recognition performance, the semi-supervised discriminant analysis algorithm is employed to map high dimensional bispectra feature data to a low dimensional subspace and identify in the low dimensional subspace by the nonlinear manifold information and partial label information of bispectra feature data. In order to verify the effect of the proposed algorithm, the same manufacturer, same batch and same type FM radios, as representative communication emitter with the same model, are used here to perform identification experiment. Experiment results show the highest recognition rate of proposed method for test sample is up to 87.6% when the labeled training FM radio samples are limited, which points to the effectiveness of this algorithm in extracting fingerprint feature and recognition accuracy on same type communication emitter identification.

Key words: communication emitter identification, feature extraction, square integral bispectra, semi-supervised discriminant analysis

CLC Number:

TN911.7

Guochuan HAN, Jinyi ZHANG, Ke LI, Likang HE, Yuxi JIANG, Tao WANG. Communication emitter identification under square integral bispectra and semi-supervised discriminant analysis[J]. Journal of Shanghai University（Natural Science Edition）, 2019, 25(5): 722-732.

Figures/Tables 8

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Communication emitter identification under square integral bispectra and semi-supervised discriminant analysis

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