GPS and navigation GPS)، GLONASS، GALILEO
Mohsen Shamirzaei; Mehran Mir Shams
Volume 14, Issue 3 , September 2021, , Pages 75-90
Abstract
The main task of the study is to estimate the position error in an inertial navigation system by integrating it with the visual system. The case study is a spacecraft that must accurately measure its position relative to a predetermined landing point. The spacecraft is assumed to be augmented GNSS navigation. ...
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The main task of the study is to estimate the position error in an inertial navigation system by integrating it with the visual system. The case study is a spacecraft that must accurately measure its position relative to a predetermined landing point. The spacecraft is assumed to be augmented GNSS navigation. Therefore, when satellite signals are dropped out or when landing on a moving marine platform, the data of the vision navigation system replaces the information of the satellite navigation system and improves the accuracy of the spacecraft navigation system. An Extended Kalman filter has been used to integrate inertial and vision navigation system information. In addition, the output data of the vision system, in order to be used in the Kalman filter measurement equations, is first processed by the recursive least square filter. The relevant relations are given and based on the results of software simulation, the efficiency of the proposed method is shown.
S. M. SalehiAmiri; A. A. Nikkhah; H. Nobahari
Volume 7, Issue 3 , October 2014, , Pages 1-8
Abstract
This paper presents a method for calculation the non observable states in alignment and calibration process in gimballed inertial navigation system, using estimation method in static linear system and heuristic optimization algorithms. The non observable constant states in alignment process are horizontal ...
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This paper presents a method for calculation the non observable states in alignment and calibration process in gimballed inertial navigation system, using estimation method in static linear system and heuristic optimization algorithms. The non observable constant states in alignment process are horizontal accelerometers biases and azimuth gyroscope drift. In order to use the estimation method in static system, the observations are recorded in necessary time duration to convert the dynamic alignment process to static process. Simulation results show appropriate accuracy of purposed method for calculation the non observable states. Although the case study is the alignment process for gimballed inertial navigation system, the purposed method can be used for calibration and alignment of any inertial navigation systems.In purposed method the genetic heuristic optimization algorithm is used.
Mahdi Jafari; Arash Sangary; Jafar Roshanyan
Volume 5, Issue 3 , October 2012, , Pages 11-19
Abstract
The Inertial navigation system is an ideal solution for motion detection with high accuracy with fast dynamics, but the precise location and status of the system output can be significantly reduced over time. On the other hand, global positioning system is able to determine its position with an average ...
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The Inertial navigation system is an ideal solution for motion detection with high accuracy with fast dynamics, but the precise location and status of the system output can be significantly reduced over time. On the other hand, global positioning system is able to determine its position with an average accuracy around the earth. But the GPS alone isn’t enough for navigation of orbital modules, because it doesn’t have situation of orbital modules. The integrated inertial navigation system with global positioning system is a low cost method of providing an accurate and reliable navigation system in the civilian and military aerospace applications. In this paper, using the extended Kalman filter, we design an algorithm to estimate error of sensors, navigation and GPS. This method can be widely used in the integrated navigation INS / GPS in aerospace applications and provides an accurate navigation.
