Paper Info
Title
Multipath estimation using an intelligent optimization algorithm with non-Gaussian noise
Abstract
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.
Year
DOI
Venue
2017
10.23919/IConAC.2017.8081971
2017 23rd International Conference on Automation and Computing (ICAC)
Keywords
Field
DocType
multipath estimation,optimization,central error entropy criterion,non-Gaussian noise
Kernel (linear algebra),Multipath propagation,Mathematical optimization,Differential evolution,Multipath mitigation,Optimization algorithm,Linear programming,Gaussian noise,Optimization problem,Mathematics
Conference
ISBN
Citations 
PageRank 
978-1-5090-5040-6
0
0.34
References 
Authors
5
4
Name
Order
Citations
PageRank
Lan Cheng111.37
hong yue2378.00
Gang Xie300.34
Mifeng Ren4167.85