Abstract
The advancements in technology have made global positioning system (GPS) part and parcel of human daily life. Apart from its domestic applications, GPS is used as a position determination system in the field of defence for guiding missiles, navigation of ships, landing aircrafts, etc. These systems require precise position estimate and is only possible with the reduced measurement uncertainty and efficient navigation solution. Due to its robustness to noisy measurements and exceptional performance in wide range of real-time applications, Kalman filter (KF) is used often in defence applications. In order to meet the increase in demands of defence systems for high precise estimates, the KF needs upgradation, and this paper proposes a new covariance update method for conventional Kalman filter that improves its performance accuracy. To evaluate the performance of this developed algorithm called modified variance Kalman filter (MVKF), real-time data collected from GPS receiver located at Andhra University College of Engineering (AUCE), Visakhapatnam (Lat/Lon: 17.72°N/83.32°E) is used. GPS statistical accuracy measures (SAM) such as distance root mean square (DRMS), circular error probability (CEP), and spherical error probability (SEP) are used for performance evaluation.
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