Microwave photonics frequency measurement with improved accuracy based on an artificial neural network

Abstract

Photonics-assisted techniques for microwave frequency measurement (MFM) show great potential for overcoming electronic bottlenecks, with wild applications in radar and communication. The MFM system based on the stimulated Brillouin scattering (SBS) effect can measure the frequency of multiple high-frequency and wide-band signals. However, the accuracy of the MFM system in multi-tone frequency measurement is constrained by the SBS bandwidth and the nonlinearity of the system. To resolve this problem, a method based on an artificial neural network (ANN) is suggested, which can establish a nonlinear mapping between the measured two-tone signal spectra and the theoretical frequencies. Through simulation verification, the ANN optimized frequencies within the range of (0.5, 27) GHz of the MFM system show 79%, 76%, 70%, 44% reduction in errors separately under four spectral signal-to-noise ratios (SNR) conditions, 20 dB, 15 dB, 10 dB, 0 dB, and the frequency resolution is improved from 30 MHz to 10 MHz.