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.