Fault-tolerant Inertial Measuring Instrument with Neural Network

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.