Open AccessSpecific radar emitter identification based on two stage multiple kernel extreme learning machine
Author(s) -
Shi Ya,
Yuan MingDong,
Ren Junlin,
Xu Shengjun
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12231
Subject(s) - artificial intelligence , pattern recognition (psychology) , radar , kernel (algebra) , discriminative model , classifier (uml) , kernel method , computer science , radial basis function kernel , kernel fisher discriminant analysis , extreme learning machine , support vector machine , variable kernel density estimation , discriminant , algorithm , mathematics , artificial neural network , telecommunications , combinatorics
To make full use of the discriminative information containing in the whole ambiguity function (AF) plane, a novel two stage multiple kernel extreme learning machine (TSMKELM) method for specific radar emitter identification is proposed. Firstly, the AF plane is segmented into the non‐overlapping Doppler shift stripes and each stripe is encoded as a kernel. Next, the discrimination of these stripes is evaluated and sorted according to the kernel discriminant ratio (KDR) criterion, which is in line with the large margin principle of KELM. Then, only the stripes with large KDRs are kept and the combined kernel is calculated by directly using the normalized KDRs as combination weights. At last, the KELM classifier is employed to fulfil the individual identification task. The proposed algorithm, named as KDR‐TSMKELM, solves the kernel combination weights and kernel classifier parameter separately, bringing much efficiency in practice. Experiments on two real radar datasets validate the proposed algorithm.