Wiener Filter Aided Second-order Cyclostationary Feature Detection of Mixed Signals for Higher Recognition Precision

Document Type

Article

Publication Date

4-14-2021

Identifier/URL

40873220 (Pure)

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Abstract

The cyclostationary analysis has been used to detect mixed signals for recognizing signals. However, the noises existent in signals lower the recognition precision. In this paper, we propose to utilize the Wiener filter to reduce the noise variance, thereby improving the mixed signal detection precision. In our de-sign, we first utilize the Wiener filter to suppress the noise existent in received signals. Then we extract the statistical spectral characteristics of mixed signals by calculating the spectral correlation function (SCF) to identify different modulation types. Moreover, we provide theoretical analysis of the detection performances, including the derivation of the autocorrelation functions of mixed signals, and the proof that the noise variance could be effectively reduced by applying the Wiener filter. Consequently, the mixed signal detection precision could be improved. Furthermore, we conduct simulations to verify the effectiveness of the proposed design, and the results demonstrate that the detection precision of the proposed detection scheme is higher than the counterpart system without using the Wiener filter.

DOI

10.1109/ISMICT51748.2021.9434937

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