Aerospace Science and Technology
Seyyed Reza Ghaffari-Razin; Reza Davari-Majd; Behzad Voosoghi; Navid Hooshangi
Abstract
Computerized Ionospheric Tomography (CIT) is a method to reconstruct ionospheric electron density image by computing Total Electron Content (TEC) values from the recorded GPS signals. Due to the poor spatial distribution of GPS stations, limitations of signal viewing angle and discontinuity of observations ...
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Computerized Ionospheric Tomography (CIT) is a method to reconstruct ionospheric electron density image by computing Total Electron Content (TEC) values from the recorded GPS signals. Due to the poor spatial distribution of GPS stations, limitations of signal viewing angle and discontinuity of observations in time and space domain, CIT are an inverse ill-posed problem. In order to solve these problems, two new methods are developed and compared with the initial method of Residual Minimization Training Neural Network (RMTNN). Modified RMTNN (MRMTNN) and Ionospheric Tomography based on the Neural Network (ITNN) is considered as new methods of CIT. In all two methods, Empirical Orthogonal Functions (EOFs) are used to improve accuracy of vertical domain. Also, Back Propagation (BP) and Particle Swarm Optimization (PSO) algorithms are used to train the neural networks. To apply the methods for constructing a 3D-image of the electron density, 23 GPS measurements of the International GNSS Service (IGS) with different geomagnetic indexes are used. For validate and better assess reliability of the proposed methods, 4 ionosondee stations have been used. Also the results of proposed methods have been compared to that of the NeQuick empirical ionosphere model. Based on the analysis and comparisons, the RMSE of the ITNN model at high geomagnetic activity in DOUR, JULI, PRUH and WARS ionsonde stations are 1.22, 1.46, 1.18 and 1.19 (1011 ele./m3), respectively. The results show that RMSE of the ITNN model is less than other models in both high and low geomagnetic activities and in ionosonde stations.
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.
