Incremental predictive Kalman filter for alignment of inertial navigation system

Fathi, Mahdi; Ghahramani, Nematollah; Ashtiani, Mohammad Ali Shahi; Mohammadi, Ali; Fallah, Mohsen

doi:10.1177/0954410018794324

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…It is imperative to emphasize that the KF is solely applicable in situations where a linear equation can represent the state space model, specifically the state transition function. This suggests that the temporal progression of the system's state can be represented graphically as a linear trend [16]. The EKF determines the state of the system by means of a feedback-integrated control mechanism.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Ath Thahirah Al Azhima,

Hakim,

Nulfatwa

et al. 2024

IJEECS

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Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.

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Section: The Proposed Methodsmentioning

confidence: 99%

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Ath Thahirah Al Azhima,

Hakim,

Nulfatwa

et al. 2024

IJEECS

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show abstract

“…where R N is the meridian radius of curvature, R E is transverse radius curvature, ' is latitude, and h is the altitude. By augmenting equations (14) and (20) to (22), the discrete modeling of the dynamic system with the above-mentioned state vector will be…”

Section: Dcm-ekf Based On Velocity Observationmentioning

confidence: 99%

“…New mechanization in the pseudo-geographic frame is discussed in Liu et al 18 that eliminates the effect of linear movement errors on the heading's error by decoupling. Using improved robust Huber Cubarure Kalman filter, incremental predictive Kalman filter and adaptive cubature Kalman filtering are addressed in Zhang et al, 19 Fathi et al, 20 and Gao et al, 21 respectively.…”

Section: Introductionmentioning

confidence: 99%

A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems

Ghanbarpourasl

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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A new robust quaternion Kalman filter is developed for accurate alignment of stationary strapdown inertial navigation system. Most fine alignment algorithms have tried to estimate the biases of gyroscopes and accelerometers to reduce the errors of the alignment process. In stationary platforms, due to fixed inputs for sensors, the summation of various errors such as fixed bias, misalignment, scale factor, and nonlinear errors acts like one bias error, and then the identification of each error will be impossible. The observability of gyros and accelerometers’ biases has also been studied. But, nowadays, we know that all of these unknown parameters are not observable. Then this problem can increase the complication of the alignment algorithm. The accelerometers’ errors mainly affect the errors of the roll and pitch angles, but a big portion of the heading’s error results from the gyroscopes’ errors. Modeling of all errors as additional states without considering the observability parameters has no benefits, but will increase the filter’s dimension, so the filter’s performance will decrease. In this study, due to the observability problem, a new robust multiplicative quaternion Kalman filter is designed for the alignment of a stationary platform. The presented algorithm does not estimate the sensors’ errors, but it is robust to uncertainty in the sensors’ errors. In the proposed scheme, the bounds of parameters’ errors are introduced to filter, and the filter tries to remain robust with respect to these uncertainties. The method uses the benefits of quaternions in attitude modeling, and then the robust filter is adapted to work with quaternions. The ability of the new algorithm is evaluated with MATLAB simulations. The outcomes show that the presented algorithm is more accurate than other traditional methods. The extended Kalman filter with accelerometers’ outputs and the horizontal velocities as the measurement equations and additive quaternion Kalman filter are used for comparisons.

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Generalized Incremental Predictive Filter for Integrated Navigation System INS/GPS in Tangent Frame

Ghahremani

Alhassan

2022

jocee

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Incremental predictive Kalman filter for alignment of inertial navigation system

Cited by 3 publications

References 15 publications

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter

A new robust quaternion-based initial alignment algorithm for stationary strapdown inertial navigation systems

Generalized Incremental Predictive Filter for Integrated Navigation System INS/GPS in Tangent Frame

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