I am a doctoral researcher at the Institute of Geodesy (IfE), Leibniz Univerisity Hannover.
Currently, I’m with the research training group (RTG) Integrity and Collaboration in Dynamic Sensor Networks (i.c.sens, GRK 2159), funded by the German Research Foundation (DFG). In the RTG, my research topic is Bounding and Propagating Uncertainty with Interval Mathematics. The aim is to investigate the uncertainty for GNSS with interval mathematics and set theory, based on which, an alternative integrity approach will be developed for future autonomous navigation applications.
Before joining IfE and i.c.sens, I earned my Master of Science degree in Earth Oriented Space and Science Technology (ESPACE) from Technical University of Munich (TUM). I defended my Master’s thesis on Precise Point Positioning with Ambiguity Resolution for Different GNSS Signals under the supervision of Prof. Urs Hugentobler and Dr. Bingbing Duan in 2020.
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Ph.D. candidate in Geodesy and Geoinformation, since 2020
Leibniz University Hannover, Germany
M.Sc in Earth Oriented Space Science and Technology (ESPACE), 2020
Technical University of Munich, Germany
B.Sc in Geophysics, 2015
Wuhan University, China
Highlights from my employment & experience.
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Selected conference and journal papers. See all research works »
There is currently a lack of comprehensive, real-world, multi-vehicle datasets fostering research on cooperative applications such as object detection, urban navigation, or multi-agent SLAM. In this paper, we aim to fill this gap by introducing the novel LUCOOP dataset, which provides time-synchronized multi-modal data collected by three interacting measurement vehicles.
This paper aims to evaluate the performance of the set-based fault detection. This approach differs from probabilistic residual-based (RB) or solution separation (SS) fault detection and exclusion methods utilized in the Receiver Autonomous Integrity Monitoring (RAIM) and Advanced RAIM.
GNSS integrity monitoring requires proper bounding to characterize all ranging error sources. Unlike classical approaches based on probabilistic assumptions, our alternative integrity approach depends on deterministic interval bounds as inputs. The intrinsically linear uncertainty propagation with intervals is adequate to describe remaining systematic uncertainty, the so-called imprecision. In this contribution, we make a proposal on how to derive the required intervals in order to quantify and bound the residual error for empirical troposphere models, based on the refined sensitivity analysis via interval arithmetic. This will contribute to a realistic uncertainty assessment of GNSS-based single point positioning.
In this contribution, we aim to demonstrate the feasibility of applying the alternative integrity approach to autonomous navigation in terms of several key aspects, i.e., the handling of GNSS multipath effect in the urban environment, fault detection and exclusion, and further consideration of applying weighting models.
For safety critical applications like autonomous driving, high trust in the reported navigation solution is mandatory. This trust can be expressed by the navigation performance parameters, especially integrity. Multipath errors are the most challenging error source in GNSS since only partial correction is possible. In order to ensure high integrity of GNSS-based urban navigation, signal propagation mechanisms and the potential error sources induced by the complex measurement environment should be sufficiently understood.
This paper introduces two deterministic approaches for GNSS uncertainty bounding and compares them with the conventional least-squares method theoretically and experimentally with simulated and real measurements.
Integrity monitoring is of great importance for Global Navigation Satellite Systems (GNSS) applications. Unlike classical approaches based on probabilistic assumptions, the alternative interval-based integrity approach depends on deterministic interval bounds as inputs. Different from a quadratic variance propagation, the interval approach has intrinsically a linear uncertainty propagation which is adequate to describe remaining systematic uncertainty. In order to properly characterize all ranging error sources and determine the improved observation interval bounds, a processing scheme is proposed in this contribution.
Talks at conferences, seminars and workshops. See all research works »
My supervisors at universities & internships, former and current colleagues and collaborators