Frequency super-resolution time-frequency analysis method in the optical downconversion E-field measurement system

Because of the challenges of an electromagnetic space with vast information, rapid dynamics, and wide bandwidth, the optical downconversion E-field measurement system can be used for reducing measurement time and enhancing signal reception by downconverting signals to a narrowband. However, the narrowband compression often causes spectral aliasing, hindering the effective extraction of parameters as traditional separation methods fail to work with aliased signals. In order to address the above issues, we propose a frequency super-resolution time-frequency analysis method (FSR-TFAM) for downconversion of aliased signals based on eigenvalue decomposition. On the one hand, dense signals in the frequency domain are expanded with time to realize the sparsity of the dense spectra and retain the signal parameters to the greatest extent. On the other hand, the method of separating signals from the noise subspace according to their eigenvalues is adopted to solve the problem of time-frequency contradiction in the traditional time-frequency analysis. Also, under the condition of limited sampling length, this method can read the frequency information of the downconversion signal with high precision, which greatly improves the measurement accuracy of the system. In single-tone signal simulations, the proposed method could reduce the mean frequency error by 98.08% compared to short-time Fourier transform (STFT).