A joint algorithm of hopping period estimation for frequency-hopping signals

摘要

Parameter estimation is an important component in the field of frequency-hopping communication. In particular, the accuracy and the efficiency of the hopping-cycle estimation is significant for these applications. The traditional time-frequency method, e.g., Short Time Fourier Transform, cannot work well with high resolution of both time and frequency, according to Heisenberg's uncertainty principle. In this paper we propose a novel algorithm which is based on Short Time Fourier Transform (STFT) and Sparse Linear Regression (SLR). Firstly, the signal is preprocessed by STFT and the information of peaks is extracted by a first-order differential method. Secondly, the hopping segment data is processed with the SLR according to the dual sparsity of time-frequency of the hopping signal. Finally, combining the statistical transition moments, an accurate estimate of the jump cycle is achieved. Simulation results demonstrate that the estimation algorithm is more accurate and efficient in low SNR than the traditional STFT.