Combining a global navigation satellite system (GNSS) and microelectromechanical technology-based inertial system (MINS) has become essential in land vehicle navigation systems. In an urban environment, the error of position output by GNSS receiver increases because of the blocking and reflection of the signal by buildings, which affects the positioning accuracy of the integrated system. In order to improve the positioning accuracy under these scenarios, this paper proposed a new robust method based on the estimation of the standard deviation of GNSS positioning error. In our study, a method for estimating the standard deviation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> (or variance) of the position error of GNSS receiver is firstly proposed. Then a robust filtering method combined multi-factor scaling and bias estimation is proposed based on the estimation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> . The simulations and vehicle navigation test show that the proposed method has a robust positioning effect. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> increases, the position accuracy is closed to the Sage-Husa filtering method with boundary constraint (SH-KF). Simulations also indicate that the proposed method has better robust effect than the traditional Kalman filter and SH-KF when a large bias error of position exists. 

A New Robust Filtering Method of GNSS/MINS Integrated System for Land Vehicle Navigation

The explosive growth of road vehicles especially the private cars has brought unprecedented pressure to a series of problems in urban transportation systems, such as traffic congestion and environmental pollution. Private cars trajectory data and perceiving their information provide a promising solution to these problems. However, the collection of large-scale trajectory data for private cars with high accuracy and reliability is still delicate tasks in urban environments. In this paper, we propose a low-cost and user-friendly implementation method for achieving large-scale private cars trajectory data acquisition via designing lightweight GPS module and On Board Diagnostics (OBD) reader. To ensure reliable trajectory data acquisition via GPS/OBD integration, we propose an ensemble learning based Gauss Process Regression (GPR) method so as to cope with the non-linearity, non-stationarity and incremental training problems during trajectory collection. We design a classification-type loss (CTL) function and build a regression to classification (R2C) method with Learn++ for realizing ensemble learning. The proposed approach implements incremental learning when new trajectory data arrives and is able to resolve the concept drifting problem. Experiments in real-world urban environment have demonstrated the effectiveness and reliability of the proposed method, it achieves better trajectory prediction performance than the comparative methods under various road conditions in GPS-denied areas. 

Trajectory Data Acquisition via Private Car Positioning Based on Tightly-coupled GPS/OBD Integration in Urban Environments

Combining a global navigation satellite system (GNSS) and microelectromechanical technology-based inertial system (MINS) has become essential in land vehicle navigation systems. In an urban environment, the error of position output by GNSS receiver increases because of the blocking and reflection of the signal by buildings, which affects the positioning accuracy of the integrated system. In order to improve the positioning accuracy under these scenarios, this paper proposed a new robust method based on the estimation of the standard deviation of GNSS positioning error. In our study, a method for estimating the standard deviation <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> (or variance) of the position error of GNSS receiver is firstly proposed. Then a robust filtering method combined multi-factor scaling and bias estimation is proposed based on the estimation of <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula>. The simulations and vehicle navigation test show that the proposed method has a robust positioning effect. When <inline-formula><tex-math notation="LaTeX">${\boldsymbol{\sigma}}^p$</tex-math></inline-formula> increases, the position accuracy is closed to the Sage-Husa filtering method with boundary constraint (SH-KF). Simulations also indicate that the proposed method has better robust effect than the traditional Kalman filter and SH-KF when a large bias error of position exists.

Aiming at the characteristics of vehicle positioning, a new vehicle positioning method based on the rotation modulation (RM) technology, kinematic constraint (KC), and the robust adaptive Kalman filter (RAKF) is proposed. On the one hand, on the basis of RM inertial navigation system and KC, this method achieves the whitening of colored measurement noise by improving the measurement equation; on the other hand, the RAKF algorithm is used to suppress the influence of the dynamic model error and observation anomaly on the estimation of the state parameters. Finally, the proposed new method is applied to the practical vehicle positioning system. Compared with the results of KF or MEMS-RMINS, the maximum position error of the proposed method by using RAKF is less than that of the KF algorithm or MEMS-RMINS, and the positioning accuracy is improved by 26.7% than that of the KF algorithm and only 21.87% of the MEMS-RMINS. At the same time, when the system has the observation anomaly, this method can also ensure the positioning accuracy of the system. The experimental results show that the new method can effectively suppress the influence of the system model error and observation anomaly to improve the vehicle positioning accuracy.

New Vehicle Positioning Method Based on RMINS/KC and Robust Adaptive KF

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Zhen Hou

Author Tools

Chat about Author

Papers

New Vehicle Positioning Method Based on RMINS/KC and Robust Adaptive KF