Showing 4 results for Pseudo-Range
M H Refan, A Dameshghi, M Kamarzarrin,
Volume 9, Issue 4 (12-2013)
Abstract
Differential base station sometimes is not capable of sending correction information for minutes, due to radio interference or loss of signals. To overcome the degradation caused by the loss of Differential Global Positioning System (DGPS) Pseudo-Range Correction (PRC), predictions of PRC is possible. In this paper, the Support Vector Machine (SVM) and Genetic Algorithms (GAs) will be incorporated for predicting DGPS PRC information. The Genetic Algorithm is employed to feature subset selection. Online training for real-time prediction of the PRC enhances the continuity of service on the differential correction signals and therefore improves the positioning accuracy in Real Time DGPS. Given a set of data received from low cost GPS module, the GASVM can predict the PRC precisely when the PRC signal is lost for a short period of time. This method which is introduced for the first time for prediction of PRC is compared to other recently published methods. The experiments show that the total RMS prediction error of GASVM is less than 0.06m for on step and 0.16m for 10 second ahead cases
M. R. Mosavi, S. Azarshahi, I. Emamgholipour , A. A. Abedi,
Volume 10, Issue 1 (3-2014)
Abstract
In present study, using Least Squares (LS) method, we determine the position smoothing in GPS single-frequency receiver by means of pseudo-range and carrier phase measurements. The application of pseudo-range or carrier phase measurements in GPS receiver positioning separately can lead to defects. By means of pseudo-range data, we have position with less precision and more distortion. By use of carrier phase data, we do not have absolute position and just dislocation is available, but the accuracy is high. In present research, we have combined pseudo-range and carrier phase data using LS method in order to determine GPS receiver's position smoothing. The results of comparison by LS method show less RMS error, less calculation volume and more smoother in using carrier phase-pseudo-range data together relative to pseudo-range data in isolation.
P. Teymouri, M. R. Mosavi, M. Moazedi,
Volume 14, Issue 3 (9-2018)
Abstract
Due to widespread use of Global Positioning System (GPS) in different applications, the issue of GPS signal interference cancelation is becoming an increasing concern. One of the most important intentional interferences is spoofing signals. An effective interference (delay spoof) reduction method based on adaptive filtering is developed in this paper. The principle of method is using adaptive filters to eliminate interference, obtain an estimate of interfering signal and subtract that from the corrupted signal. So, what remains in the output is the desired signal. Here, for updating the filter coefficients adaptive algorithms in both time (statistical and deterministic) and transform domain will be studied. The proposed adaptive filter is applied to a batch of spoofing GPS data in pseudo-range level. The results indicate that all investigated algorithms are able to reduce positioning steady-state miss-adjustment up to 70 percent. In this context, the variable step-size least mean square algorithm performs better than others do.
Nerjes Rahemi, Kurosh Zarrinnegar, Mohammad Reza Mosavi,
Volume 21, Issue 3 (8-2025)
Abstract
In determining position using GPS, due to local effects, pseudo-range errors cannot be mitigated by methods such as the use of reference stations or mathematical models; however, by using precise carrier phase observations and deploying a statistically optimal filter such as Phase-Adjusted Pseudo-range (PAPR) algorithm, the error can be significantly reduced. Additionally, the correlation between observations is a factor affecting positioning accuracy. In this paper, by using both pseudo-range and carrier phase observations and taking into account the effect of spatial correlation between observations to determine the variance-covariance matrix, the accuracy of position determination using the recursive Least Squares method is increased. For this purpose, the PAPR algorithm was implemented to reduce error. Next, a non-diagonal variance-covariance matrix was introduced to estimate the variance of the observations based on their spatial correlations. Experimental results on real data show that the proposed method improves positioning accuracy by at least 10% compared to previous methods. To evaluate the complexity of the proposed models, we employed an ARM STM32H743 processor. The findings indicate a modest increase in the proposed model complexity compared to earlier models, along with a substantial improvement in positioning accuracy.
