Navigation Solutions for GPS Receiver Position Estimation over the Southern Region of India
P Sirish Kumar1, V B S Srilatha Indira Dutt2

1P Sirish Kumar, Department of Electronics & Communications Engineering, Aditya Institute Of Technology And Management, Tekkali,Srikakaulam, India.
2V B S Srilatha Indira Dutt, Department of Electronics & Communications Engineering , GITAM University, Visakhapatnam, India.

Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1672-1675 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2853037619/19©BEIESP
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Abstract: GPS is a mechanism of attaining the position of any object on or above the earth surface. There are many applications GPS is emerging, which require accuracy in the GPS position estimate, ranging from meters to centimeter level accuracy. The accuracy of GPS position estimate is influenced by various factors like satellite geometry, ionospheric delay and tropospheric delay, various multi-path effects, number of satellites in view and navigational solution employed. Many of the above factors do not have static behavior globally, and need to be examined regionally to provide a precise solution. This paper mainly focuses on implementation of Least Square Estimator (LSE) and Kalman filter (EKF) on the data possessed with dual-frequency Global Positioning System receiver placed at Indian Institute of Science, Bangalore (13.0210N/77.50E) to provide with suitable navigational algorithm over Southern area of Indian Sub Continent. The algorithm performance based on 2D and 3D statistical position accuracy measures Circular Error Probability (CEP), Spherical Error Probability (SEP), Distance Root Mean Square Error (DRMS) and Confidence Level (CL 40 mts) is evaluated to characterize their performance over this region. This paper is worked towards providing a precise navigational solution over a region and not concerned to a particular application
Keywords: Navigation solution, Least Square Estimator and Kalman filter
Scope of the Article: Smart Solutions – Wearable Sensors and Smart Glasses