Parameter estimation of frequency hopping signal based on MWC-MSBL reconstruction

Aiming at the problem that single-network hopping signals have not fully utilised its frequency domain sparse characteristic in the parameter estimation, this study proposes a parameter estimation of frequency hopping (FH) signal based on multi-measurement vector sparse Bayesian learning (MSBL) in modulation wideband converter (MWC). Since the FH signal is sparse in the frequency domain, the authors apply the MSBL method to estimate its parameters. After the signal is sampled by the MWC, the MSBL algorithm is used to reconstruct its support set. Then the time–frequency ridge method is used to estimate the signal's hop duration, time-hopping, and carrier frequency based on the time–frequency map. Simulation experiments show that under the condition of low signal-to-noise ratio, the parameter estimation performance in the case can be improved by up to 65% and anti-noise performance can be improved up to 6 db compared with the existing method. The result is very close to the Nyquist full sampling and can greatly improve the accuracy of the FH signal parameter estimation in the MWC system and relieve the pressure of the hardware.