Journal Article10.1109/TCSII.2020.3025455
GNSS Vector Tracking Method Using Graph Optimization
18
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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Random Forest-Based Optimization Algorithm for Shipborne GNSS Vector Tracking Loop
Wei Liu,Kaiwei Zhu,Yuan Hu,Naiyuan Lou,Tsung-Hsuan Hsieh,Shengzheng Wang +5 more
Cooperative Smartphone GNSS/PDR for Pedestrian Navigation
Changhui Jiang,Yuwei Chen,Shoubin Chen,Qian Meng,Yuming Bo,Juha Hyyppa +5 more
- 01 Jun 2023
TL;DR: In this paper , the authors proposed a cooperative PDR/GNSS integration method with Factor Graph Optimization (FGO) to represent the relationship between the multiple agents' states, measurements and interranging information.
An Optimization-Based Indoor-Outdoor Seamless Positioning Method Integrating GNSS RTK, PS, and VIO
JiangBo Song,Wanqing Li,Chufeng Duan,Lei Wang,Yiheng Fan,Xiangwei Zhu +5 more
TL;DR: An optimization-based indoor-outdoor seamless positioning method integrating GNSS RTK, PS, and VIO achieves accurate global positioning information in complex indoor-outdoor transition scenarios.
References
G 2 o: A general framework for graph optimization
Rainer Kümmerle,Giorgio Grisetti,Hauke Strasdat,Kurt Konolige,Wolfram Burgard +4 more
- 09 May 2011
TL;DR: G2o, an open-source C++ framework for optimizing graph-based nonlinear error functions, is presented and demonstrated that while being general g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems.
2.5K
A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices
TL;DR: Simulation results for a target tracking example illustrate that the proposed VBAKF has better robustness to resist the uncertainties of process and measurement noise covariance matrices than existing state-of-the-art filters.
A Novel Robust Student's t -Based Kalman Filter
TL;DR: A novel robust Student's t-based Kalman filter is proposed by using the variational Bayesian approach, which provides a Gaussian approximation to the posterior distribution.
A Novel Robust Gaussian–Student's t Mixture Distribution Based Kalman Filter
TL;DR: The novel GSTM distributed Kalman filter has the important advantage over the RSTKF that the adaptation of the mixing parameter is much more straightforward than learning the degrees of freedom parameter.
239
Performance Analysis of Vector Tracking Algorithms for Weak GPS Signals in High Dynamics
TL;DR: This paper explores the ability of vector tracking algorithms to track weak Global Positioning System (GPS) signals in high dynamic environments and finds that vector-based methods can perform better than traditional methods in environments with high dynamics and low signal power.
211