Effect of Noise Covariance Matrices on State of Charge Estimation Using Extended Kalman Filter

Adaikkappan, Maheshwari; Nageswari, S.

doi:10.1080/03772063.2022.2055657

Cited by 3 publications

(1 citation statement)

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“…The system state covariance estimation update in the EKF aims to continuously refine the covariance matrix, providing a more accurate depiction of the uncertainty in the system's state variables as new measurements are incorporated into the filtering process [24], [25]. In addition to the state estimation, the output of the EKF system is the estimation of the system state covariance using (9).…”

Section: System State Covariance Estimation Updatementioning

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: System State Covariance Estimation Updatementioning

confidence: 99%