A Non-Data-Aided Feedforward Timing Estimator Based on Multiple Cyclic Correlations for Short-Term Burst Signals

Abstract

Due to short frames of short-term burst signals, the performance of conventional non-data-aided feedforward timing estimators is unsatisfactory. This letter investigates an estimator based on multiple cyclic correlations addressing this issue. The cyclic correlations at high-valued timing lags provide the timing information for estimating timing offset. Motivated by it, we combine the timing information at various timing lags, utilizing the second-order statistical characteristics of the limited sample data for accurate estimation. Considering the estimation range of roll-off factors and computational complexity, an asymptotically unbiased estimation using four correlations is deduced. Simulation results show that the proposed estimator outperforms other estimators.