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Book ChapterDOI

Fractional Order Extended Kalman Filter for Attitude Estimation

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.
Citations
More filters
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.
References
More filters
Journal ArticleDOI
TL;DR: The results show that it is necessary to model fractional noise in order to consistently predict the bias of a modern MEMS gyro, but the complexity of the Kalman filter approach makes other methods, such as the moving averages, appealing.
Abstract: MEMS gyroscopes are gaining popularity because of their low manufacturing costs in large quantities. For navigation system engineering, this presents a challenge because of strong nonstationary noise processes, such as 1/f noise, in the output of MEMS gyros. In practice, on-the-fly calibration is often required before the gyroscope data are useful and comparable to more expensive optical gyroscopes. In this paper, we focus on an important part of MEMS gyro processing, i.e., predicting the future bias given calibration data with known (usually zero) input. We derive prediction algorithms based on Kalman filtering and the computation of moving averages, and compare their performance against simple averaging of the calibration data based on both simulations and real measured data. The results show that it is necessary to model fractional noise in order to consistently predict the bias of a modern MEMS gyro, but the complexity of the Kalman filter approach makes other methods, such as the moving averages, appealing.

71 citations

Journal ArticleDOI
TL;DR: Estimation schemes for discrete fractional and integer order state-space systems with fractional order colored noise and proposed estimation algorithm additionally uses information about noise dynamics, which allows for obtaining better estimates of state vector.
Abstract: The paper presents estimation schemes for discrete fractional and integer order state-space systems with fractional order colored noise. The fractional order colored noise is a generalization of the traditional colored noise (noise with dynamic dependency) for the case when the dynamics of noise is of fractional order. Proposed estimation algorithm additionally uses information about noise dynamics, which allows for obtaining better estimates of state vector. The numerical experiments of estimation integer order system with fractional noise are presented as well.

48 citations

Journal ArticleDOI
TL;DR: The fractional-order stochastic chaotic Chen system is presented and the results show the effectiveness of the proposed method for chaotic signal cryptography.

33 citations

Journal ArticleDOI
TL;DR: A new proof method based on the above involved relationship for the non existence of periodic solutions of rational fractional order linear time invariant systems is derived and it is proved to be equivalent to an integer order linearTime invariant non-autonomous system.
Abstract: Existence of periodic solutions and stability of fractional order dynamic systems are two important and difficult issues in fractional order systems U+0028 FOS U+0029 field. In this paper, the relationship between integer order systems U+0028 IOS U+0029 and fractional order systems is discussed. A new proof method based on the above involved relationship for the non existence of periodic solutions of rational fractional order linear time invariant systems is derived. Rational fractional order linear time invariant autonomous system is proved to be equivalent to an integer order linear time invariant non-autonomous system. It is further proved that stability of a fractional order linear time invariant autonomous system is equivalent to the stability of another corresponding integer order linear time invariant autonomous system. The examples and state figures are given to illustrate the effects of conclusion derived.

22 citations

Journal ArticleDOI
TL;DR: The history and basics of complementary filters are included with examples, the concepts of filtering based on fractional-order calculus are applied to the complementary filter, and the efficacy of non-integer-order filtering on systems with non-Gaussian noise is explored with good success.
Abstract: Orientation estimation is very important for development of unmanned aerial systems (UASs), and is performed by combining data from several sources and sensors. Kalman filters are widely used for this task, however they typically assume linearity and Gaussian noise statistics. While these assumptions work well for high-quality, high-cost sensors, it does not work as well for low-cost, low-quality sensors. For low-cost sensors, complementary filters can be used since no assumptions are made with regards to linearity and noise statistics. In this article, the history and basics of complementary filters are included with examples, the concepts of filtering based on fractional-order calculus are applied to the complementary filter, and the efficacy of non-integer-order filtering on systems with non-Gaussian noise is explored with good success.

21 citations