Iterative Maximum Likelihood Estimators for High-Dynamic GNSS Signal Tracking

Luo

2015

Sci. China Inf. Sci.

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 global navigation satellite system (GNSS), iterative maximum likelihood estimation (IMLE), joint navigation state error discriminator, Cramer-Rao bound (CRB), vector tracking loop CitationWang Y Q, Luo Y, Wang P, et al. A novel joint navigation state error discriminator based on iterative maximum likelihood estimation.

“…The navigation state error is the deviation between the user's real and estimated states. Substituting (13) and (14) into (12), the MLE of the navigation state error can be derived aŝ…”

Section: Joint Navigation State Error Discriminatormentioning

confidence: 99%

Section: Iterative Maximum Likelihood Estimationmentioning

confidence: 99%

See 1 more Smart Citation

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

Luo

2015

Sci. China Inf. Sci.

“…When there exists non‐line‐of‐sight (NLOS) propagation, multipath mitigation technology (MMT) can separate multipath component . Unlike early‐late receiver, the maximum likelihood (ML) estimator was proposed by considering a random model of received sequence . When prior statistics is known at the receiver, the maximum posteriori (MAP) probability estimation can enhance the positioning accuracy significantly .…”

Section: Introductionmentioning

confidence: 99%

“…29 Unlike early-late receiver, the maximum likelihood (ML) estimator was proposed by considering a random model of received sequence. 30 When prior statistics is known at the receiver, the maximum posteriori (MAP) probability estimation can enhance the positioning accuracy significantly. 31 Newton iteration maximum likelihood estimation is an efficient way for multipath mitigation which proves to approach the Cramer-Rao bound (CRB).…”

Section: Introductionmentioning

confidence: 99%

Unambiguous and precise correlation receiver for binary offset carrier modulated signal

Zhang

Zuo

2019

Int J Communication

Summary The binary offset carrier (BOC) modulated signal can improve the positioning accuracy and increase the multipath resistance in global navigation satellite system (GNSS), and it may cause potential ambiguity in the process of signal acquisition and code tracking. In this paper, a simple but efficient unambiguous receiver is firstly proposed for multiple side‐peaks mitigation by implementing correlation of the received BOC signal with local sine wave instead of square wave used at the transmitter. Moreover, the potential degradation of sharpness of the nonlinear correlation induced by the sine wave is well compensated by optimizing the early‐to‐late spacing. The other reason leading to ambiguity is the multipath propagation, so we further propose a maximum likelihood (ML) estimator with Newton iteration method, where the received GNSS signal is modeled via the line‐of‐sight (LOS) component and the first‐arrived non‐line‐of‐sight (NLOS) component. Finally, the analytical expression of multipath propagation Cramer‐Rao bound is derived for the designed ML estimator. Simulation results indicate that compared with the conventional BOC modulation, the proposed sine wave receiver can achieve unambiguous and more precise code tracking performance and thus turns out to be more robust to multipath propagation.

Robust Unambiguous Estimation of High-Order BOC Signals: The DOME Approach

et al. 2016

This paper presents a novel unambiguous estimation technique of high‐order BOC signals, dubbed the Double Optimization Multi‐correlator‐based Estimator (DOME). The DOME is a robust alternative to other unambiguous BOC estimation techniques found in the literature, being able to operate in harsh propagation conditions typical of urban environments, while fully exploiting the BOC signal properties. The approach followed consists of the solution of two parallel and dependent optimization problems in which N samples of the correlation function are exploited, solving the ambiguity problem at the same time that the high‐order BOC accuracy is preserved. The preliminary performance assessment of the DOME in both controlled and realistic propagation conditions is presented, demonstrating the robustness and accuracy of the approach proposed. Copyright © 2016 Institute of Navigation