Abstract
Present radar signal emitter recognition approaches suffer from a dependency on prior information. Moreover, modern emitter recognition must meet the challenges associated with low probability of intercept technology and other obscuration methodologies based on complex signal modulation and must simultaneously provide a relatively strong ability for extracting weak signals under low SNR values. Therefore, the present article proposes an emitter recognition approach that combines ensemble empirical mode decomposition (EEMD) with the generalized S-transform (GST) for promoting enhanced recognition ability for radar signals with complex modulation under low signal-to-noise ratios in the absence of prior information. The results of Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise are reported. The results verify that EEMD suppresses the occurrence of mode mixing commonly observed using standard empirical mode decomposition. In addition, EEMD is shown to extract meaningful signal features even under low SNR values, which demonstrates its ability to suppress noise. Finally, EEMD-GST is demonstrated to provide an obviously better time-frequency focusing property than that of either the standard S-transform or the short-time Fourier transform.
Highlights
RADAR signal emitter recognition is an important aspect of electronic warfare reconnaissance systems that seeks to identify individual radar emitters through an analysis of the electromagnetic signals and thereby determine vital information regarding the technical level, performance, position, and deployment conditions of enemy radar systems for supporting decision making regarding enemy weapon systems and targets [1]
ensemble empirical mode decomposition (EEMD)-generalized S-transform (GST) processing was applied to a mixed signal involving the linear superposition of several typical radar signals with additive white Gaussian noise, and the analysis results obtained were compared with the corresponding
The present study eliminated this dependency on prior information by combining EEMD with the GST to realize the robust recognition of radar signal emitters
Summary
RADAR signal emitter recognition is an important aspect of electronic warfare reconnaissance systems that seeks to identify individual radar emitters through an analysis of the electromagnetic signals and thereby determine vital information regarding the technical level, performance, position, and deployment conditions of enemy radar systems for supporting decision making regarding enemy weapon systems and targets [1]. Radar emitters have been recognized by methods employing a secondary treatment level of data, for example, by constructing pulse sample figures based on vague sets [12] and by constructing an emitter recognition model with reference to rough set theory [13, 14] While these methods possess the advantages of easy operation and realization, they suffer from the loss of weak signal features, which makes it difficult to conduct fine recognition. The GST is applied to the IMFs obtained by decomposition, and the time-frequency planar features of signals are characterized and extracted using the good time-frequency focusing property of GST for realizing effective radar signal emitter recognition These advantages of the proposed EEMD-GST approach are verified by Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise
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