摘要
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.
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.
基金
supported by National High Technology Research and Development Program of China(863)(Grant No.2013AA1548)
关键词
最大似然估计
状态误差
鉴别器
HESSIAN矩阵
导航
迭代
极大似然估计
成本函数
global navigation satellite system （GNSS）, iterative maximum likelihood estimation （IMLE）, jointnavigation state error discriminator, Cramer-Rao bound （CRB）, vector tracking loop
