Abstract:
The last two decades have shown in increasing trend in the use of nanigation technologies such as Strapdown Inertial Systems (SDINS) in several applications including land vehicles and automated car navigation. On the other hand it can cause large position errors over short time, due to the ...
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Abstract:
The last two decades have shown in increasing trend in the use of nanigation technologies such as Strapdown Inertial Systems (SDINS) in several applications including land vehicles and automated car navigation. On the other hand it can cause large position errors over short time, due to the low quality of the Inertial Measurement Unit (IMU). These errors determine the performance and the navigation accuracy of the INSs. Although the huge efforts to improve SDINSin terms of its mechanization equations,it could not cover the remaining drawbacks of SDINS; such as the impact of INS short term errors ,model dependency ,prior knowledge dependency , sensor dependency , and computational errors. This paper proposed an intelligent navigator to overcome the limitation of existing INS algorithms. The intelligent navigator is based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed conceptual intelligent navigator consisted of SDINS architecture that was developed using adaptive fuzzy system networks to acquire the navigation knowledge. In addition, a navigation information Database ,and a window-based learned parameters updating method were implemented to store and accumulate navigation knowledge.
Abstract:
The integration of global positioning system (GPS) and Inertial Navigation System (INS) are continuously gaining interests in many positioning and navigation applications. Both systems have their unique features and shortcomings. Their integration offers systems that overcome each of their ...
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Abstract:
The integration of global positioning system (GPS) and Inertial Navigation System (INS) are continuously gaining interests in many positioning and navigation applications. Both systems have their unique features and shortcomings. Their integration offers systems that overcome each of their drawbacks and maximize each of their benefits. An INS/GPS integration method based on Artificial Neural Networks (ANNs) to fuse INS measurements and GPS measurements has been suggested. It is also provide high performance INS/GPS integration with accurate prediction for position
And velocity components during GPS signal absence. Thus the integration of the two systems presents a number of advantages and overcomes each systems inadequacy. An ANN was adopted in this paper using position and velocity update architectures and utilizing the window based weight updating strategy to updates the navigation knowledge in the strategy using two data test IMU systems.
