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Author

Nimmi Sharma

Bio: Nimmi Sharma is an academic researcher from VIT University. The author has contributed to research in topics: Gyroscope & Extended Kalman filter. The author has co-authored 1 publications.

Papers
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Book ChapterDOI
06 Dec 2018
TL;DR: The Fractional Order Extended Kalman Filter (FKF) approach is designed for estimating attitude with the help of inertial sensors in the attitude heading and reference system architecture.
Abstract: Attitude estimation is one of the core frameworks for a vehicle navigating with the help of inertial sensors such as accelerometer, gyroscope and magnetometer. Measurements obtained by these sensors are fused together to obtain vehicle attitude in the form of roll, pitch and yaw angles. Several state estimation frameworks have been proposed in the literature of which the extended Kalman filter and the complementary filtering based schemes are most popular. In this paper, the Fractional Order Extended Kalman Filter (FKF) approach is designed for estimating attitude with the help of inertial sensors in the attitude heading and reference system architecture. The FKF scheme is applied on the sensor data captured from commercial navigation units and compared with reference attitude for analysis. The simulations are carried out for varying fractional orders of different states and the corresponding results depict the dependency of estimation accuracy on system order.

1 citations


Cited by
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Proceedings ArticleDOI
02 Dec 2022
TL;DR: In this paper , different configurations of analog filters are tested in order to possibly find a better solution than that offered by the algorithm, used in many low-cost applications of Unmanned Aerial Vehicle positional awareness.
Abstract: This work deals with the comparison between different analog implementations of complementary filters with a digital processing algorithm. For this purpose, different configurations of analog filters are tested in order to possibly find a better solution than that offered by the algorithm, used in many low-cost applications of Unmanned Aerial Vehicle positional awareness.