ML-based frequency estimation and synchronization of frequency hopping signals

A maximum likelihood (ML)-based algorithm for frequency estimation and synchronization of frequency hopping signals is proposed in this paper. By using a two-hop signal model that 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 (TFD) and the wavelet-based algorithms in papers by Barbarossa and Scaglione (1997) and by Khalil and Hippenstiel (1996), the new ML algorithm does not require the selection of a kernel or mother wavelet function. In addition, compared with the TFD-based algorithm, it has a better performance with a lower implementation complexity.