Fault-tolerant Inertial Measuring Instrument with Neural Network
Olha Sushchenko, Yurii Bezkorovainyi, Volodymir Golitsyn
Abstract
The paper deals with the development of the algorithm for processing redundant information in the airborne non-collinear measuring instrument taking into account the possibility of faults. The research is based on such methods as neural networks, and processing information in redundant non-collinear inertial measuring instruments. As a result, the algorithm of redundant information processing assigned for use on the moving vehicle during its operation is developed. The proposed solution is acceptable for unmanned aerial vehicles due to a decrease in the computational burden.
Topics & Concepts
Artificial neural networkInertial frame of referenceComputer scienceFault toleranceInformation processingInertial navigation systemInertial measurement unitData processingInertial reference unitReal-time computingArtificial intelligenceDistributed computingNeurosciencePhysicsQuantum mechanicsBiologyOperating systemAdvanced Control and Stabilization in Aerospace SystemsTechnology and Human Factors in Education and HealthAdvanced Research in Science and Engineering