Exact Frequency Estimation in the i.i.d. Noise via KL Divergence of Accumulated Power
Weitao Sun, Huigang Wang, Qingyue Gu, Shaowei Rong, Liming Fan · IEEE Communications Letters · 2021
To solve the frequency estimation deterioration caused by independent and identically distributed (i.i.d.) noise, this letter proposes an exact and robust frequency estimation algorithm based on the accumulated phase-difference power on Kullback-Leibler divergence (APP-KLD). First, the frequency estimation problem is modeled by the accumulated power of phase-difference between the receiving signal and Fourier basis function. Furthermore, the true frequency is estimated iteratively by maximizing the Kullback–Leibler divergence, which measures the distance between the accumulated power and a constant reference vector. Simulations demonstrate the robust performance of the APP-KLD algorithm at various levels of heavy-tailed noise and sample numbers.