Carrier estimation algorithm based on novel hybrid particle filtering

To reduce the computation complexity and improve the weight degeneracy and loss of diversity about the particles in particle-filtering (PF) based carrier estimation, a novel hybrid particle filter based carrier estimation algorithm was proposed and the method for assigning particle weights of the proposed PF algorithm (PPF) was derived. In this algorithm, the m-order Monte Carlo Markov chain was introduced and the optimal importance function-the posterior importance function was approximated by a weighted sum of non-zero mean Gaussian distributions and the iterative computation of the particles was constrained by the maximum a posterior (MAP) criterion. In non-Gaussian environment, by applying the algorithm in the non-cooperative receiver of the time division multiple address/differential quadrature phase shift keying (TDMA/DEQPSK) data frame in low earth orbit (LEO) satellite communication system, it is found that the efficiency of the particles is improved and the computational complexity is reduced compared to the carrier estimation algorithm based on the traditional PF.