Abstract
In this paper an advanced method for the navigation system correction of a spacecraft using an error prediction model of the system is proposed. Measuring complexes have been applied to determine the parameters of a spacecraft and the processing of signals from multiple measurement systems is carried out. Under the condition of interference in flight, when the signals of external system (such as GPS) disappear, the correction of navigation system in autonomous mode is considered to be performed using an error prediction model. A modified Volterra neural network based on the self-organization algorithm is proposed in order to build the prediction model, and the modification of algorithm indicates speeding up the neural network. Also, three approaches for accelerating the neural network have been developed; two examples of the sequential and parallel implementation speed of the system are presented by using the improved algorithm. In addition, simulation for a returning spacecraft to atmosphere is performed to verify the effectiveness of the proposed algorithm for correction of navigation system.
Highlights
The autonomous navigation system of spacecraft can be used to control the spacecraft without relying on groundbased support and to determine the position, speed, and altitude of spacecraft in real time by measurement equipment aboard
They have justified the use of generalpurpose computing on graphics processing units (GPGPU) for decision support system [15] and developed a general formulation of the prediction and estimation problems for Mathematical Problems in Engineering a class of weakly structured problems using interval neural networks and genetic algorithms
This paper presents an advanced algorithmic method for increasing the accuracy of an inertial navigation system (INS) of spacecraft
Summary
The autonomous navigation system of spacecraft can be used to control the spacecraft without relying on groundbased support and to determine the position, speed, and altitude of spacecraft in real time by measurement equipment aboard. E proposed a development of decision support system based on neural networks and a genetic algorithm They have justified the use of generalpurpose computing on graphics processing units (GPGPU) for decision support system [15] and developed a general formulation of the prediction and estimation problems for Mathematical Problems in Engineering a class of weakly structured problems using interval neural networks and genetic algorithms. He showed two examples of application of the developed system for solving urgent problems [16]. The last section discusses the computer simulation results considering flight of a return spacecraft and the conclusions are given
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