A Fast Estimation Algorithm for Parameters of Multiple Frequency-Hopping Signals Based on Compressed Spectrum Sensing and Maximum Likelihood

Abstract

The parameter estimation of multiple frequency-hopping (multiple FH) signals with frequency-switching time is a great challenge under conditions in which the number of signals is unknown. Due to the increasing mobility of devices such as unmanned aerial vehicles (UAVs), speed of parameter estimation is even more demanding. To solve this problem, an algorithm for estimating parameters of multiple FH signals based on compressed spectrum sensing and maximum likelihood (CSML) theory is proposed in this paper. First, the received signal is split into segments of the same length, and the frequencies contained in each segment are extracted using compressed spectrum sensing and kurtosis threshold. Next, the frequencies contained in adjacent segments are compared to find the signal segment in which frequency hopping occurs and its corresponding frequency. Finally, a hopping-time fast estimation algorithm based on the maximum likelihood theory is used to estimate the hopping time. Simulation results show that the algorithm proposed in this paper can estimate the parameters of multiple FH signals quickly and accurately when the number of signals is unknown and that it is equally effective for multiple FH signals with frequency-switching time.