Existing approaches to closely integrating inertial navigation systems and a global navigation satellite system for mobile objects are based on the principle of redundancy. Kalman filtering is the most commonly applied mathematical tool, despite the fact that both the system itself and its measurement model are not ideal. By contrast, the use of situational control methods, which compare each class of situation to a clear, already known solution, enables us to abandon Kalman filtering and the problems associated with its implementation, in favor of an algorithm using an expert system to compile navigation information. This article describes the operation of a loosely-coupled navigation system based on satellite navigation and low-cost inertial MEMS sensors, with a fuzzy logic expert system acting as an aggregation algorithm.
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Navigation System Based on the Fuzzy Logic Expert System
Semantic Scholar · Computer Science · 2019
Abstract
Existing approaches to closely integrating inertial navigation systems and a global navigation satellite system for mobile objects are based on the principle of redundancy. Kalman filtering is the most commonly applied mathematical tool, despite the fact that both the system itself and its measurement model are not ideal. By contrast, the use of situational control methods, which compare each class of situation to a clear, already known solution, enables us to abandon Kalman filtering and the problems associated with its implementation, in favor of an algorithm using an expert system to compile navigation information. This article describes the operation of a loosely-coupled navigation system based on satellite navigation and low-cost inertial MEMS sensors, with a fuzzy logic expert system acting as an aggregation algorithm.