Optimal GNSS Signal Tracking Loop Design Based on Plant Modeling

Conventional research for signal tracking of the Global Navigation Satellite System (GNSS) uses a loop filter to minimize the effect of measurement noise. Although for a few decades, research into the optimal GNSS tracking loop has been based on similarity between signal tracking and general control loop theory, it has mainly focused on optimal estimator, or shown vulnerability for high dynamic signal tracking. To enhance the performance of the optimal signal tracking loop, this paper proposes new plant modeling for optimal GNSS signal tracking that consists of both optimal estimator and controller. The proposed plant modeling is able to maximize the performance of the optimal signal tracking loop due to the relationships between code and carrier tracking, and between the frequency and phase of the carrier. In addition, the plant clearly defines a relationship between the general control loop and GNSS tracking loop, so that the plant is ready to be applied to various control theories for GNSS signal tracking. To assess the performance of the proposed plant modeling, we implement a linear quadratic Gaussian (LQG) tracking loop based on the new proposed plant and process simulation data. Comparison of the processing results to those of conventional research shows improved performance of the proposed plant modeling.

[1]  Nesreen I Ziedan,et al.  Bayesian Filtering Approaches for Unambiguous BOC Tracking under Weak Signal Conditions , 2011 .

[2]  Mark L. Psiaki,et al.  Extended Kalman Filter Methods for Tracking Weak GPS Signals , 2002 .

[3]  Ronald A. Iltis,et al.  C/A Code Tracking and Acquisition with Interference Rejection using the Extended Kalman Filter , 1999 .

[4]  Gene F. Franklin,et al.  Feedback Control of Dynamic Systems , 1986 .

[5]  Michael Patrick Fikes,et al.  GPS receiver tracking loop optimization using l1 theory , 1994 .

[6]  Audrey Giremus,et al.  Multi-channel extended Kalman filter for tracking BOC modulated signals in the presence of multipath , 2005 .

[7]  Yu-Hsuan Chen,et al.  Robust GNSS Signal Tracking Against Scintillation Effects: A Particle Filter Based Software Receiver Approach , 2010 .

[8]  Gyu-In Jee,et al.  Comparison of GPS Tracking Loop Performance in High Dynamic Condition with Nonlinear Filtering Techniques , 2008 .

[9]  Changdon Kee,et al.  Optimal signal tracking algorithm for GNSS signal using moving set-point LQG system , 2013 .

[10]  James L. Garrison,et al.  Extended Kalman Filter-Based Tracking of Weak GPS Signals under High Dynamic Conditions , 2004 .

[11]  Yu Morton,et al.  A Variable Gain Adaptive Kalman Filter-Based GPS Carrier Tracking Algorithm for Ionosphere Scintillation Signals , 2010 .

[12]  Douglas P. Looze.  Franklin, Powell and Emami-Naeini, Feedback Control of Dynamic Systems, 6 th Edition, Prentice-Hall, 2010. (referred to as FPE) References: Ogata, Modern Control Engineering, Prentice-Hall, 2009. Dorf, Modern Control Systems, Prentice-Hall, 2008. , 2013 .

[13]  Y. J. Lee,et al.  A GPS C/A Code Tracking Loop Based on Extended Kalman Filter with Multipath Mitigation , 2002 .

[14]  B. Anderson,et al.  Optimal control: linear quadratic methods , 1990 .

[15]  P. Crosta,et al.  Improvement of a High-Grade GNSS Receiver Robustness Against Ionospheric Scintillations Using a Kalman Filter Tracking Scheme , 2015 .

[16]  Nesreen I. Ziedan,et al.  Multi-Frequency Combined Processing for Direct and Multipath Signals Tracking Based on Particle Filtering , 2011 .

[17]  Changdon Kee,et al.  The development of modularized post processing GPS software receiving platform , 2008, 2008 International Conference on Control, Automation and Systems.

[18]  V. Barreau,et al.  Kalman Filter based robust GNSS signal tracking algorithm in presence of ionospheric scintillations , 2012 .

[19]  Steven P. Powell,et al.  Kalman-Filter-Based Semi-Codeless Tracking of Weak Dual-Frequency GPS Signals , 2003 .

[20]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .