Journal Article10.1109/TCSII.2020.3025455
GNSS Vector Tracking Method Using Graph Optimization
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TL;DR: A graph optimization (GO) method is employed to substitute the KF to estimate the navigation solutions in VT, which is expected to alleviate the influence of the measurement model nonlinearity on the state estimation.
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Abstract: Commonly, there are two different types of signal tracking methods in Global Navigation Satellite System (GNSS) receivers: Scalar Tracking (ST) and Vector Tracking (VT). In ST, each tracking channel works independently and generates the measurements for Position, Velocity and Time (PVT) estimation. On the contrary, VT accomplishes the signal tracking and PVT solutions estimation together using a Kalman filter (KF). In this manner, mutual aiding between these channels are obtained, which contributes to the superior performance of VT than ST. KF is the method in the manner of weighted average of predicted state and the measurements derived state. Two drawbacks restrict the KF performance, (1) in KF, correlation between current state and all the past states is ignored, only adjacent state is included in the current state estimation through state transformation matrix; (2) linear KF might degrade the estimation due to the nonlinearity of the VT measurement model. In this brief, a graph optimization (GO) method is employed to substitute the KF to estimate the navigation solutions in VT. In the GO-VT, the measurements and the state transformation from past epochs are all regarded as constraints to optimize the states estimation. With the iterations during the optimization, the GO-VT is expected to alleviate the influence of the measurement model nonlinearity on the state estimation. A field test was carried out for assessing the performance of the GO-VT, its superior position accuracy compared with that from the KF supported that the GO could enhance the VT.
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Citations
A Study on Graph Optimization Method for GNSS/IMU Integrated Navigation System Based on Virtual Constraints
Haiyang Qiu,Yun Zhao,Hui Wang,Lei Wang +3 more
- 27 May 2024
TL;DR: A graph optimization method for GNSS/IMU integrated navigation system based on virtual constraints effectively utilizes historical pseudorange information and maintains high positioning accuracy despite satellite occlusion and non-line-of-sight conditions.
1
Performance Enhancement and Evaluation of a Vector Tracking Receiver using Adaptive Tracking Loops
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- 28 Dec 2023
TL;DR: The proposed vector tracking receiver using adaptive tracking loops significantly enhances navigation performance in weak signal and high dynamic scenarios compared to traditional scalar tracking loops.
1
A Study on Graph Optimization Method for GNSS/IMU Integrated Navigation System Based on Virtual Constraints
TL;DR: In instantaneous performance testing, the proposed graph optimization-based GNSS/IMU model with virtual constraints maintains an RMSE error within 5% compared with real pseudorange measurement, while in a continuous performance testing scenario with no available GNSS signal, the method shows approximately a 30% improvement in horizontal RMSE accuracy over the traditional graph optimization method.
A Graph Optimization Method for GNSS Signal Tracking Loop
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- 17 Jun 2022
TL;DR: In this article , a Graph Optimization (GO) method based carrier tracking errors mitigation method was proposed, which utilized the inherent relationship between the tracking errors in time domain, and the phase tracking errors decreased by xx.
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