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 ...
Read More
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. R. Mosavi; A. R. Baziar; M. Moazedi
Volume 10, Issue 2 , September 2013, , Pages 9-16
Abstract
Spoofing could pose a major threat to Global Positioning System (GPS) navigation, so the GPS users have to gain an in-depth understanding of GPS spoofing. Since spoofing attack can influence position results, spoof compensation is possible through reducing position deviations. In this paper, a novel ...
Read More
Spoofing could pose a major threat to Global Positioning System (GPS) navigation, so the GPS users have to gain an in-depth understanding of GPS spoofing. Since spoofing attack can influence position results, spoof compensation is possible through reducing position deviations. In this paper, a novel processing technique is proposed and the wavelet transform is used to eliminate the impact of spoofing on the stationary GPS receivers. We assumed that the spoofing attack was immediately detected, and then the position residuals of the last authentic and new spoofing signals were passed to the statistic wavelet transform at the first level. By denoising in the next step, position deviations due to the spoofing attack can be extracted. Then, the estimated position solution of the received signal is corrected. Finally, the receiver coordinates are calculated by averaging the corrected positions. For validation of the suggested algorithm, five different data sets are investigated. We mitigated the spoofing in all data sets more than 93%. The test results show that the proposed technique supremely improves the performance of the GPS receiver and attenuates the spoofing effect.
M. saraf; M.R. Mosavi; K. Mohammadi
Volume 9, Issue 2 , September 2012
Abstract
In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. ...
Read More
In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient Descent (GD) in terms of the input feature vectors. The probability density of all feature vectors can help to optimize the learning rates of RBFNN by applying GMM. Another possibility is to utilize the Evolutionary Algorithms (EAs) to find the optimum solution. However, EAs often behave randomly which canandrsquo;t be mathematically controlled. So, a combined RBFNN based on novel PE algorithm has been proposed which has a soft behavior through the learning of non-linear function. The PE algorithm defines the occurrence probability of local minima in the space of extracted features as a Gaussian distribution correspondence to each chromosome. Then, it estimates the entire probabilities of local minima in an iterative procedure. These techniques have been utilized in the application of robust satellites subset selection. Geometric Dilution of Precision (GDOP) is the main factor to estimate the strength of goodness of each satellites subset. Then, the subset with the lowest value has been selected for improving the positioning performance, but it is so non-linear and has computational burden to navigation systems. These techniques have been implemented and the results on measured GPS data demonstrate that it significantly track the non-linearity of GPS GDOP comparison with the other conventional approaches.
Mohammad Reza Mosavi; Azadeh Nakhaei; Sh. Bagherinia
Volume 7, Issue 2 , September 2010, , Pages 139-150
Abstract
Global Positioning System (GPS) is proven to be an accurate positioning sensor. However, there are several sources of errors such as ionosphere and troposphere effects, satellite time errors, errors of orbit data, receivers errors, and errors resulting from multi-path effect which reduce the accuracy ...
Read More
Global Positioning System (GPS) is proven to be an accurate positioning sensor. However, there are several sources of errors such as ionosphere and troposphere effects, satellite time errors, errors of orbit data, receivers errors, and errors resulting from multi-path effect which reduce the accuracy of low-cost GPS receivers. These sources of errors also limit the use of single-frequency GPS receivers due to their less accurate data. Therefore, its important to reduce the effect of errors on GPS systems. In order to cope with these errors and enhance GPS systems accuracy, Differential GPS (DGPS) method can be used. The problem with this method is slow updating process of differential corrections. In this paper, three algorithms based on Kalman Filtering (KF) are proposed to predict real-time corrections of DGPS systems. The efficiency of proposed algorithms is verified based on the collected of actual data. The experimental results carried out in field tests assure the high potential of these methods to get accurate positioning data. The results show that KF with variable transition matrix is better than other methods; so that its possible to reduce the Root Mean Square (RMS) of positioning errors in low-cost GPS receivers to less than one meter.