An Xin, Yang Zhangyi, Liu Zuoheng, Zhang Youdi, Dong Wei
Appl Opt. 2024 Apr 1;63(10):2535-2542. doi: 10.1364/AO.519402.
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.
用于微波频率测量（MFM）的光子辅助技术在克服电子瓶颈方面显示出巨大潜力，在雷达和通信领域有广泛应用。基于受激布里渊散射（SBS）效应的MFM系统可以测量多个高频和宽带信号的频率。然而，MFM系统在多音频率测量中的精度受到SBS带宽和系统非线性的限制。为了解决这个问题，提出了一种基于人工神经网络（ANN）的方法，该方法可以在测量的双音信号频谱与理论频率之间建立非线性映射。通过仿真验证，MFM系统在（0.5, 27）GHz范围内经ANN优化后的频率在四种频谱信噪比（SNR）条件下，即20 dB、15 dB、10 dB、0 dB时，误差分别降低了79%、76%、70%、44%，并且频率分辨率从30 MHz提高到了10 MHz。
