چكيده به لاتين
Inertial Navigation Systems (INS) are one of the most commonly used navigation systems that provide information about the position and speed of an instrument using inertial sensors. But the problem is that this type of navigation system can not be used alone and for a long time due to an increase in our error rate. On the other hand, the GPS system or global positioning system is also a kind of navigation system that provides accurate information about the speed and position of a receiver whenever and wherever it is. Thus, with the benefit of these two types of navigation systems, the GPS / INS comprehensive navigation system can increase the accuracy and the ability to navigate. There are several ways to combine these two navigation systems, which is the use of neural networks in the implementation of a comprehensive navigation system of one of these types of methods.
In this research, navigation is a terrestrial moving device that has been used to achieve its position in a variety of ways, all of which have a low-coupling connection and are subject to loop arches. As a basic method, simulation is performed using conventional Kalman filter and the results are recorded for comparison with the methods presented in this study. Using the UKF Nonlinear Kalman Toolkit, the simulation method is simulated, followed by other methods for integration. Integration using a Kalman filter and matching algorithm using the adaptive-fuzzy Kalman filter are further introduced and simulated in this research.
Also, using the RBF neural network, the simulation of the GPS integration with the INS was initially simulated using Innovative methods combined with the Kalman filter and the neural network. Finally, with the completion of the Kalman filter combination and the neural network, a method is proposed whereby the GPS signal can be interrupted by using the trained neural network to obtain a moving position with good precision than the INS.
