ML-Based Forward Symbol Timing Offset Estimation for Burst Signals

In order to ensure the performance of burst signal demodulation, forward symbol timing offset estimation algorithm is usually used for symbol timing recovery. Aiming at the shortcomings of common forward symbol timing estimation algorithms, a new algorithm based on maximum likelihood estimation joint trigonometric polynomial interpolation is proposed in this article, which is suitable for the data of four samples per symbol and can resist large frequency offset. Hereafter, in order to make it more suitable for engineering practice, optimize it into an improved data-aided (DA) forward symbol timing offset estimation algorithm with multiple characteristics, e.g., moderate frequency offset capture range, insensitive to shaping coefficient, relatively low complexity, excellent estimation performance, flexible algorithm structure and less sample data required for calculation. The simulation results show that the performance of the improved algorithm in this article can approach the modified cramer-rao bound (MCRB) within the frequency offset capture range, under the conditions of low signal to noise ratio (SNR) and small shaping coefficient.