Aerospace Science and Technology
Amir Moghtadaei Rad
Abstract
Inertial navigation amplifies the noise of the input sensors over time due to the presence of an integrator in the output path to determine the position and attitude of the object. This system has high bandwidth and good short-term accuracy. On the other hand, GPS navigation has low bandwidth, low noise ...
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Inertial navigation amplifies the noise of the input sensors over time due to the presence of an integrator in the output path to determine the position and attitude of the object. This system has high bandwidth and good short-term accuracy. On the other hand, GPS navigation has low bandwidth, low noise processing power, and long-term accuracy. However, it can only determine the position and does not give us information about the object's attitude. Most papers have presented integrated algorithms related to GPS/INS tightly coupled navigation and have provided relatively acceptable results. Nevertheless, the main problem in this integration model is when there is an intentional or stochastical signal interference for GPS, which is not far from the mind in military applications. Therefore, navigation faces a problem. This article provides a solution with a tightly coupled integrated algorithm for high accuracy in integrated navigation.
Aerospace Science and Technology
Ali Khavari; S. Mohammad Reza Moosavi; Amir Tabatabaee; Hadi Shahriyar Shahhosseini
Volume 12, Issue 2 , October 2019, , Pages 11-18
Abstract
Abstract: Urban canyon is categorized as hard environment for positioning of a dynamic vehicle due to low number and also bad configuration of in-view satellites. In this paper, a tuning procedure is proposed to adjust the important factors in Kalman Filter (KF) using Genetic Algorithm (GA). The authors ...
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Abstract: Urban canyon is categorized as hard environment for positioning of a dynamic vehicle due to low number and also bad configuration of in-view satellites. In this paper, a tuning procedure is proposed to adjust the important factors in Kalman Filter (KF) using Genetic Algorithm (GA). The authors tested the algorithm on a dynamic vehicle in an urban canyon with hard condition and compared the results with traditional KF and Weighted Least Square (WLS) methods. The outputs showed that this algorithm could be more reliable more than 114% and 61% against WLS and traditional KF. ---------------------------------------------------------------------------------------Abstract: Urban canyon is categorized as hard environment for positioning of a dynamic vehicle due to low number and also bad configuration of in-view satellites. In this paper, a tuning procedure is proposed to adjust the important factors in Kalman Filter (KF) using Genetic Algorithm (GA). The authors tested the algorithm on a dynamic vehicle in an urban canyon with hard condition and compared the results with traditional KF and Weighted Least Square (WLS) methods. The outputs showed that this algorithm could be more reliable more than 114% and 61% against WLS and traditional KF.
