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A novel joint navigation state error discriminator based on iterative maximum likelihood estimation
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DOI：10.1007/s11432-015-5438-z.png)
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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.
Keyword:
global navigation satellite system (GNSS)
iterative maximum likelihood estimation (IMLE)
joint navigation state error discriminator
Cramer-Rao bound (CRB)
vector tracking loop
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