ML-based frequency and frequency transition time estimation of frequency hopping signals

An ML-based algorithm for frequency estimation and synchronization of frequency hopping signals is proposed in this paper. By using a two-hop signal model which incorporates the unknown hop transition time, the likelihood function of the received frequency hopping signal is formulated. A new iterative method is then derived to estimate the hopping frequencies and hop transition time. Without using any pilot signal or sync bit, the new algorithm is able to implement synchronization and frequency estimation at the same time. Unlike the time-frequency distribution and the wavelet-based algorithms, the new ML algorithm does not require one to select a kernel or mother wavelet function and thus is robust in performance.