A novel joint navigation state error discriminator based on iterative maximum likelihood estimation

Yongqing Wang; Yu Luo; Pai Wang; Siliang Wu

doi:10.1007/s11432-015-5438-z

A novel joint navigation state error discriminator based on iterative maximum likelihood estimation

Yongqing Wang^*, Yu Luo, Pai Wang, Siliang Wu

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation (IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function, gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.

Original language	English
Article number	122201
Journal	Science China Information Sciences
Volume	58
Issue number	12
DOIs	https://doi.org/10.1007/s11432-015-5438-z
Publication status	Published - Dec 2015

Keywords

Cramer-Rao bound (CRB)
Global navigation satellite system (GNSS)
Iterative maximum likelihood estimation (IMLE)
Joint navigation state error discriminator
Vector tracking loop

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10.1007/s11432-015-5438-z

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title = "A novel joint navigation state error discriminator based on iterative maximum likelihood estimation",

abstract = "To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation (IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function, gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.",

keywords = "Cramer-Rao bound (CRB), Global navigation satellite system (GNSS), Iterative maximum likelihood estimation (IMLE), Joint navigation state error discriminator, Vector tracking loop",

author = "Yongqing Wang and Yu Luo and Pai Wang and Siliang Wu",

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year = "2015",

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T1 - A novel joint navigation state error discriminator based on iterative maximum likelihood estimation

AU - Wang, Yongqing

AU - Luo, Yu

AU - Wang, Pai

AU - Wu, Siliang

N1 - Publisher Copyright: © Siene China Press and Springer-Verlag Berlin Heidelberg 2015.

PY - 2015/12

Y1 - 2015/12

N2 - To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation (IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function, gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.

AB - To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation (IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function, gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.

KW - Cramer-Rao bound (CRB)

KW - Global navigation satellite system (GNSS)

KW - Iterative maximum likelihood estimation (IMLE)

KW - Joint navigation state error discriminator

KW - Vector tracking loop

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DO - 10.1007/s11432-015-5438-z

M3 - Article

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SN - 1674-733X

VL - 58

JO - Science China Information Sciences

JF - Science China Information Sciences

IS - 12

M1 - 122201

ER -

A novel joint navigation state error discriminator based on iterative maximum likelihood estimation

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Keywords

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