The spectral correlation theory of cyclostationary time-series
Signal Processing
Online Clustering Algorithms for Radar Emitter Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Channel blind identification based on cyclostationarity and group delay
Signal Processing
Cyclostationarity: half a century of research
Signal Processing
Optimization of time and frequency resolution for radar transmitter identification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Exploiting input cyclostationarity for blind channel identificationin OFDM systems
IEEE Transactions on Signal Processing
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Radar emitter recognition plays an important role in electronic warfare (EW). Specific radar emitter recognition is the state-of-art technology of emitter recognition, which can recognize the different radar devices of the same type. It is a composite task that involves radar signal interception, modulation recognition, features extraction and classification. In this paper, first we study the unintentional modulation on pulse (UMOP) features of radar emitter. Then the iterative least-square method is introduced for estimation of the UMOP features. Because of the discriminatory capability and abundant information of cyclostationary signatures, the zero frequency slice of cyclic spectrum is used for specific radar emitter recognition. Based on these, the sequential iterative least-square (SILS) algorithm is proposed for the online recognition of radar emitters. Finally experiments on three simulation radars and eight actual intercepted radars with the same type verify the correctness and validity of the proposed method.