Multipath estimation using an intelligent optimization algorithm with non-Gaussian noise

Multipath is known to be one of the dominant error sources in high accuracy positioning systems, and multipath estimation is crucial for multipath mitigation. Most existing multipath estimation algorithms usually consider the cases of single mutlipath with Gaussian noise. However, non-Gaussian noises and two - multipath are often encountered in many practical environments. In this paper, a new algorithm is proposed to cope with the multipath estimation problem of the latter. First, the multipath estimation problem is transferred into a constrained optimization problem using the central error entropy criterion (CEEC) as its objective function. The second-order moment of the estimation error and the prior information are taken as constraints to reduce the mean of the estimation error. Then, a modified ε-constrained rank-based differential evolution (εRDE) algorithm is explored to solve the optimization problem. The proposed algorithm has been compared with the particle filter algorithm using a two-multipath case study example with non-Gaussian noises. The results suggest the proposed algorithm has improved the multipath estimation accuracy.