Online parameter and process covariance estimation using adaptive EKF and SRCuKF approaches

Riva, Mauro Hernan; Beckmann, Daniel; Dagen, Matthias; Ortmaier, Tobias

doi:10.1109/cca.2015.7320776

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Alternatively, experimental data can also be used to compute the noise variance. If measurements of the accelerometer and magnetometer were taken at rest conditions, the variance could be calculated using Equations ( 13) and (14).…”

Section: Measurement Noise Covariance Calculationmentioning

confidence: 99%

“…In these approaches, covariance matrices (CMs) Q and R values need to be tuned online while the EKF operates to improve the accuracy of the estimation, which can be affected by system and environmental disturbances. Mauro et al [14] applied the recursive prediction error (RPE) algorithm to update the covariance values online. Machine learning approaches have gained increased attention across various application areas.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation

Wondosen,

Debele,

Kim

et al. 2023

Aerospace

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In various applications, the extended Kalman filter (EKF) has been vital in estimating a vehicle’s translational and angular motion in 3-dimensional (3D) space. It is also essential for the fusion of data from multiple sensors. However, for the EKF to perform effectively, the optimal process noise covariance matrix (Q) and measurement noise covariance matrix (R) must be chosen correctly. The use of EKF has been challenging due to the need for an easy mechanism to select Q and R values. As a result, this research focused on developing an algorithm that can be easily applied to determine Q and R, allowing us to harness the full potential of EKF. Accordingly, an EKF innovation consistency statistics-driven Bayesian optimization algorithm was employed to achieve this goal. Q and R values were tuned until the expected result met the performance requirement for minimum error through improved measurement innovation consistency. The comprehensive results demonstrate that when the optimum Q and R, as tuned by the suggested technique, were used, the performance of the EKF significantly improved.

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Section: Measurement Noise Covariance Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation

Wondosen,

Debele,

Kim

et al. 2023

Aerospace

View full text Add to dashboard Cite

show abstract

“…Therefore, states and unknown parameters are estimated simultaneously. In order to ensure an appropriate excitation of input signals, a sensitivity-based approach is used [7]. Therewith, no special input signals (e. g. pseudo binary random signals) to estimate the unknown physical parameters are needed.…”

Section: Online Parameter Estimation and Controller Tuningmentioning

confidence: 99%

Integrated Parameter Management Concept for Simplified Implementation of Control, Motion Planning and Process Optimization Methods

Öltjen

Beckmann

Hansen

et al. 2016

AMM

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For complex automation tasks, the configuration and parametrization of both hardware and software components involves the main part of the required commissioning time. To simplify this process and significantly reduce the required expense, an integrated parameter and data management concept is proposed. Several advanced methods for an optimized process design simultaneously access a global parameter database. In this manner, the shared parameters are synchronized and verified to improve the performance and maximize the robustness of each individual method and, therewith, optimize the entire production process.

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“…An EKF and a SRCuKF were used to calculate the output sensitivity in [22], which is then used to estimate the unknown values. More information about the aSRUKF can be found in [28,30].…”

Section: Kalman Filters -Theory For Advanced Applications 86mentioning

confidence: 99%

“…The method was extended for nonlinear or time-varying systems using an EKF in [21]. Online covariance estimation for EKF and square-root cubature Kalman filter (SRCuKF) was presented in [22]. These methods implement a combination of a KF derivative and a recursive prediction-error method (RPEM) to estimate covariances online.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems

Riva¹,

Wielitzka²,

Ortmaier³

2018

Kalman Filters - Theory for Advanced Applications

Self Cite

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Since the initial developments in the state-space theory in the 1950s and 1960s, the state estimation has become an extensively researched and applied discipline. All systems that can be modelled mathematically are candidates for state estimators. The state estimators reconstruct the states that represent internal conditions and status of a system at a specific instant of time using a mathematical model and the information received from the system sensors. Moreover, the estimator can be extended for system parameter estimation. The resulting Kalman filter (KF) derivatives for state and parameter estimation also require knowledge about the noise statistics of measurements and the uncertainties of the system model. These are often unknown, and an inaccurate parameterization may lead to decreased filter performance or even divergence. Additionally, insufficient system excitation can cause parameter estimation drifts. In this chapter, a sensitivity-based adaptive square-root unscented KF (SRUKF) is presented. This filter combines a SRUKF and the recursive prediction-error method to estimate system states, parameters and covariances online. Moreover, local sensitivity analysis is performed to prevent parameter estimation drifts, while the system is not sufficiently excited. The filter is evaluated on two testbeds basedonanaxisserialmechanismandcomparedwiththejointstateandparameterUKF .

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Online parameter and process covariance estimation using adaptive EKF and SRCuKF approaches

Cited by 6 publications

References 14 publications

Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation

Bayesian Optimization for Fine-Tuning EKF Parameters in UAV Attitude and Heading Reference System Estimation

Integrated Parameter Management Concept for Simplified Implementation of Control, Motion Planning and Process Optimization Methods

Sensitivity-Based Adaptive SRUKF for State, Parameter, and Covariance Estimation on Mechatronic Systems

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