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
Mohammad Hossein Bayat; Mohammad Shahbazi; Bahram Tarvirdizadeh
Abstract
The use of Unmanned Aerial Vehicles (UAVs) with different features and for a variety of applications has grown significantly. Tracking generic targets, especially human, using the UAV's camera is one of the most active and demanding fields in this area. In this paper we implement two vision-based tracking ...
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The use of Unmanned Aerial Vehicles (UAVs) with different features and for a variety of applications has grown significantly. Tracking generic targets, especially human, using the UAV's camera is one of the most active and demanding fields in this area. In this paper we implement two vision-based tracking algorithms to track a human by using a 2D gimbal which can be mounted on UAVs. To ensure smooth movements and reduce the effect of common jumps on the trackers output, the gimbal motion control system is equipped with a Kalman filter followed by a proportional-derivative (PD) controller. Various experimental tests have been designed and implemented to track a human. The evaluation results show success in tracking the high speed movements with one of the algorithms and high accuracy in tracking the challenging movements in the other algorithm. Also in both methods, the tracking computation time is short enough and suitable for real-time implementation. The favorable performance of both algorithms indicate the ability of designed system to be implemented on the UAVs for practical applications.
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.
M. Kiani
Volume 10, Issue 2 , September 2013
Abstract
Three-axis-magnetometers (TAMs) are widely utilized as a key component of attitude determination subsystems and as such are considered the corner stone of navigation for low Earth orbiting (LEO) space systems. Precise geomagnetic-based navigation demands accurate calibration of the magnetometers. In ...
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Three-axis-magnetometers (TAMs) are widely utilized as a key component of attitude determination subsystems and as such are considered the corner stone of navigation for low Earth orbiting (LEO) space systems. Precise geomagnetic-based navigation demands accurate calibration of the magnetometers. In this regard, a complete online calibration process of TAM is developed in the current research that considers the combined effects of environmental and instrumental errors including biases, non-orthogonally parameters ,and the scale factors, without the need for clean room facilities. The sensor characteristics are estimated utilizing Kalman filter for a micro electro-mechanical sensor(MEMS)-based TAM standing on the experimental measured outputs in a noisy laboratory environment. Moreover, the stochastic TAM behavior is identified using the method of Allan variance analysis (AVA) through a six-hour static test. Subsequently, the nonlinear/non-Gaussian problem of attitude estimation, using a set of calibrated strap-down magnetometers is addressed utilizing the unscented particle filter (UPF), developed for the removal of colored-noise. Comparison of the estimated attitude, represented by quaternion parameters, with the true orientations demonstrates an acceptable level of accuracy of the developed calibration technique for small LEO space systems. Analysis of the root mean square error of the estimated attitude illustrates an accuracy of less than one degree for all axes. This is an ideal result, given the fact that MEMS-based magnetometers have been utilized.