Dual-Constellation Aided High Integrity and High Accuracy Navigation Filter for Maritime Applications

Liu, Shuchen; Gehrt, Jan-Jöran; Abel, Dirk; Zweigel, René

doi:10.33012/2019.16723

Cited by 9 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further steps in preprocessing are the calculation of satellite positions and velocities as the tightly coupled navigation filter needs them. Before using the preprocessed GNSS data, an integrity check is performed with the well-known receiver autonomous integrity monitoring (RAIM) algorithm, explained in more detail in Liu et al (2019).…”

Section: Gnss Preprocessingmentioning

confidence: 99%

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Benz

Gehrt

Zweigel

et al. 2023

navi

Self Cite

View full text Add to dashboard Cite

The automation of mining haulage vehicles has great potential in terms of safety and economy. The performance of autonomous vehicles depends largely on highly accurate vehicle state information. Deep mines are especially challenging, as satellite-based localization methods are reaching their limits. Therefore, we introduce a new navigation filter concept for the precise and robust localization of the haulage fleet that can handle temporary GNSS interruptions in deep open-pit mines. The multi-sensor navigation filter utilizes an inertial measurement unit and is aided by GNSS. We introduce a new optical speed sensor update within the tightly coupled unscented Kalman filter. The speed sensor measures the slip-free two-dimensional speed above ground. The filter was validated with an articulated dumper in a gravel pit. The new filter achieved a mean position error of 0.24 m during a test drive of 190 s with a simulated GNSS outage of 90 s.

show abstract

Section: Gnss Preprocessingmentioning

confidence: 99%

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Benz

Gehrt

Zweigel

et al. 2023

navi

Self Cite

View full text Add to dashboard Cite

show abstract

“…To realize FDE of GNSS aided INS, the previous works [5] and [6] has proposed a strategy based on receiver autonomous integrity monitoring (RAIM) and robust set inversion via interval analysis (RSIVIA) against faulty GNSS measurements. Based on [5] and [6], it is assumed in current publication, that a set of fault-free GNSS measurements is fed into the navigation system. Therefore, the current work concentrates on filter-based FDE for the other integrated sensor, i.e.…”

Section: Introductionmentioning

confidence: 99%

Robust State Estimation and Integrity Monitoring within Multi-Sensor Navigation System

Liu,

Wang,

Abel

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In autonomous applications, global navigation satellite systems (GNSS) aided inertial navigation system (INS) utilizing an extended Kalman filter (EKF) is the most widely investigated solution for high-rate and high-accurate vehicle states estimation. However, such navigation system suffers from poor parameterization, environment disturbances, human error, or even software and hardware failures under worstcase scenarios. In this paper, a novel scheme of multi-sensor navigation system is proposed, contributing to following research questions: 1) How to provide a reliable state estimation under minor system aberrations, i.e. improve the robustness of navigation system against e.g. inappropriate parameterization or environment disturbances; 2) How to provide system integrity against worst-case scenarios, i.e. significant system aberrations or even failures. The proposed scheme involves extended H∞ filter (EHF) for robustness enhancement, zonotope for protection level (PL) generation of the navigation solution and vehicle dynamic model aided fault detection (FD) of the inertial sensor. The designed approach is validated using the recorded data from an experimental platform called 'IRT-Buggy', which is an electrical land vehicle. The results show that the proposed scheme provides reliable integrity monitoring and accurate state estimation, under both real-world and artificial abnormalities and shows significant advantages against conventional 'GNSS+INS+EKF' approach.

show abstract

Robust state and protection-level estimation within tightly coupled GNSS/INS navigation system

2023

View full text Add to dashboard Cite

In autonomous applications for mobility and transport, a high-rate and highly accurate vehicle-state estimation is achieved by fusing measurements of global navigation satellite systems (GNSS) and inertial sensors. The state estimation and its protection-level generation often suffer from satellite-signal disturbances in urban environments and subsequent poor parametrization of the satellite observables. Thus, we propose an innovative scheme involving an extended H∞ filter (EHF) for robust state estimation and zonotope for the protection-level generation. This scheme is shown as part of a tightly coupled navigation system based on an inertial navigation system and aided by the GPS/Galileo dual-constellation satellite navigation system. Specifically, GNSS pseudorange and deltarange observables are utilized. The experimental results of post-processing a real-world dataset show significant advantages of EHF against a conventional extended Kalman filter regarding the navigation accuracy and robustness under various GNSS measurement parametrizations and environmental circumstances. The zonotope-based protection-level calculation is proven valid, computationally affordable, and feasible for real-time implementations.

show abstract

Dual-Constellation Aided High Integrity and High Accuracy Navigation Filter for Maritime Applications

Cited by 9 publications

References 0 publications

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

Robust State Estimation and Integrity Monitoring within Multi-Sensor Navigation System

Robust state and protection-level estimation within tightly coupled GNSS/INS navigation system

Contact Info

Product

Resources

About