Improving observability of vehicle's navigation parameters by means of Kalman filtering
Abstract
Introduction. This article describes the observability problem for navigation parameters of road vehicles, namely the coordinates and the heading angle, which are used by automated driving systems. The measurement quality of navigation parameters can vary substantially depending on vehicle operating conditions ranging from high precision measurements to complete unavailability of navigation data. Low quality of navigation data may render operation of automated driving systems impossible. To solve this problem, this work uses indirect measurements implemented if the form of so-called observers, which are able to identify the vehicle navigation parameters regardless of its operating conditions.
The purpose of the study is to improve observability of the vehicle navigation parameters.
Methodology and research methods. The observers of the vehicle navigation parameters were implemented using a kinematic model of vehicle’s motion and Kalman filtering. The accuracy of the elaborated observers was estimated by root mean square errors and maximum deviations from the reference data acquired by a high precision navigation system in various driving conditions.
Scientific novelty and results. The article provides a detailed analysis of the observability problem regarding the vehicle navigation parameters. It also proposes a development approach for observers of the vehicle heading angle and coordinates based on the Kalman filter. The parameters calculated by the elaborated observers were verified through a comparison with high precision measurements.
The practical significance of the work is constituted by the elaborated observers, which provide an improved quality of the vehicle navigation parameters.
About the Author
A. V. Chaplygin
Center of Intelligent Systems, Federal State Unitary Enterprise Central Scientific Research Automobile and Automotive Engines Institute (FSUE NAMI)
Russian Federation
Russian Federation
Postgraduate programming engineer.
Moscow 125438
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Review
For citations:
Chaplygin A.V. Improving observability of vehicle's navigation parameters by means of Kalman filtering. Trudy NAMI. 2020;(3):24-34. (In Russ.)
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