Adaptive particle-aided cubature Kalman filtering for GNSS/INS-based vehicle localization

Master thesis (2022)

Authors

D.G.A. den Boer Mechanical Engineering

Contributors

B.H.K. De Schutter Team Bart De Schutter - Mechanical, Maritime and Materials Engineering (supervisor 1)
Evert Klem Royal HaskoningDHV (supervisor 1)
Peter Morsink Royal HaskoningDHV (supervisor 1)
M. Kok Team Manon Kok - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2022 Dirk den Boer
Particle filter GNSS/INS-based vehicle localization Cubature Kalman filter Hybrid filtering architecture Kernel density estimation Switching strategy
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Published Date

12-01-2022

Language

English

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Faculty

Mechanical Engineering

Abstract

Accurate vehicle localization is considered to be a key element in future automated driving systems. A network of multiple sensors is employed to deliver information for this localization process. Loosely coupled integration of global navigation satellite systems (GNSS) and inertial navigation systems (INS) data is a common sensor-fusion method for such positioning. One of the problems of this approach, is that exact knowledge of the process- and measurement noise covariance matrices is often not available. The GNSS measurement noise uncertainties, in particular, are highly dynamic and, depending on the specific environment, might follow a non-Gaussian distribution. Since particle filter are known to be superior in non-Gaussian environments, a hybrid filtering variant is proposed: adaptive particle-aided cubature Kalman filtering. This algorithm compromises between a particle filter with kernel density estimation algorithm in periods of non-Gaussian GNSS noise, and a standard cubature Kalman filter in case of Gaussian GNSS noise. The results of GNSS/INS-based localization simulations indicate that the proposed adaptive particle-aided cubature Kalman filter outperforms traditional filtering methods in terms of minimal localization errors.