A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method
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-ra...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2025-08-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3551-en.pdf |
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| author | Nerjes Rahemi Kurosh Zarrinnegar Mohammad Reza Mosavi |
| author_facet | Nerjes Rahemi Kurosh Zarrinnegar Mohammad Reza Mosavi |
| author_sort | Nerjes Rahemi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d1d7ca0497814c94bd3c392010e38912 |
| institution | DOAJ |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-d1d7ca0497814c94bd3c392010e389122025-08-20T02:40:21ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902025-08-0121335513551A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares MethodNerjes Rahemi0Kurosh Zarrinnegar1Mohammad Reza Mosavi2 The authors are with the School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. The authors are with the School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. The authors are with the School of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16846-13114, Iran. 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.http://ijeee.iust.ac.ir/article-1-3551-en.pdfgpsphase-adjusted pseudo-range algorithmrecursive least squaresspatial correlationsvariance-covariance matrix. |
| spellingShingle | Nerjes Rahemi Kurosh Zarrinnegar Mohammad Reza Mosavi A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method Iranian Journal of Electrical and Electronic Engineering gps phase-adjusted pseudo-range algorithm recursive least squares spatial correlations variance-covariance matrix. |
| title | A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method |
| title_full | A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method |
| title_fullStr | A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method |
| title_full_unstemmed | A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method |
| title_short | A New Stochastic Model to Improve Positioning Accuracy of the Recursive Least Squares Method |
| title_sort | new stochastic model to improve positioning accuracy of the recursive least squares method |
| topic | gps phase-adjusted pseudo-range algorithm recursive least squares spatial correlations variance-covariance matrix. |
| url | http://ijeee.iust.ac.ir/article-1-3551-en.pdf |
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