Recursive Generalized Total Least Squares with Noise Covariance Estimation

Rhode, Stephan; Bleimund, Felix; Gauterin, Frank

doi:10.3182/20140824-6-za-1003.01568

Cited by 23 publications

(10 citation statements)

References 19 publications

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“…To this version of the algorithm, one can prefer a recursive estimation allowing an implementation of the online diagnostic procedure. In Rhode et al (2014), the authors propose an algorithm for recursively computing the total least squares. In Hassanabadi et al (2020), the same goal is pursued by adapting the principal component analysis to a recursive form.…”

Section: Characterization Of Operating Modesmentioning

confidence: 99%

Detection of operating mode changes, without a priori model and in uncertain environments

Ragot

2022

Transactions of the Institute of Measurement and Control

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Starting from the general observation that a measurement delivered by a sensor is subject to an uncertainty, its use in a decision chain must take into account this imprecise character. It is thus advisable to propagate this imprecision in all the chain of treatment and use of the measurement in question. In what follows, this principle is applied to the diagnostic function, one of the components of system monitoring. More precisely, we propose to analyze the temporal data collected on a system in order to detect and locate possible changes in the behavior of this system.

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Section: Characterization Of Operating Modesmentioning

confidence: 99%

Detection of operating mode changes, without a priori model and in uncertain environments

Ragot

2022

Transactions of the Institute of Measurement and Control

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“…The three common approaches to trajectory optimization are dynamic programming (DP), direct methods (DM) and indirect methods (IM) ( [6], pp. [5][6][7][8][27][28][29][30][31][32][33][34][35][36][37]. DP is an optimization method that finds a global optimum.…”

Section: Driver Assistance Systems For Automated Longitudinal Controlmentioning

confidence: 99%

“…Before CAN signals are used for updating the models, we remove signal noise using the polynomial Kalman smoother of [28]. In two situations the models are not adapted: First, during a vehicle standstill because the missing excitation in the data can cause the models to diverge.…”

Section: Parameter Adaption Modulementioning

confidence: 99%

Automated Longitudinal Control Based on Nonlinear Recursive B-Spline Approximation for Battery Electric Vehicles

Jauch

Bleimund

Frey

et al. 2019

WEVJ

Self Cite

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This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4% energy compared to MLC at same average velocity, and achieves a 2.6% higher average velocity than MLC at the same energy consumption.

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“…To deal with these problems inherent with the traditional FIR and IIR filters, local approximation approaches via polynomial functions offer a promising option, 49 among which the SGF can preserve signal integrity to a large extent and increase the signalto-noise ratio (SNR) without obviously distorting the original signals. Specifically, the SGF utilizes a polynomial function in terms of convolution to make sure a least-square approximation for the authentic signals within a moving fixed-length window by Savitzky 50…”

Section: A Moving Polynomial Kalman Smoothermentioning

confidence: 99%

Vehicle sideslip angle estimation for a four-wheel-independent-drive electric vehicle based on a hybrid estimator and a moving polynomial Kalman smoother

Wang

2018

Proceedings of the Institution of Mechanical Engineers, Part K:

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This paper presents a vehicle sideslip angle estimation scheme against noises and outliers in sensor measurements for a four-wheel-independent-drive electric vehicle. The proposed scheme combines a robust unscented Kalman filter estimator based on the 3-DOF vehicle dynamics model and an extended Kalman filter estimator based on the kinematic model to form a hybrid estimator through a weighting factor. The weighting factor can be dynamically adjusted in real time to optimize the overall estimation performance under different driving conditions. The main contributions of this study to the related literature lie in two aspects. Firstly, a robust unscented Kalman filter estimator was incorporated to improve the robustness of dynamics-based estimation to sensor measurement outliers. Secondly, a novel moving polynomial Kalman smoother was included to filter out the noises in sensor measurements. Co-simulations of Matlab/ Simulink and Carsim software were conducted under typical vehicle maneuvers and show that the proposed vehicle sideslip angle estimation scheme can obtain satisfied estimation results, with the moving polynomial Kalman smoother exhibiting better phase characteristics and filtering performance relative to commonly-used finite impulse response filters, and the robust unscented Kalman filter estimator being robust to sensor measurement outliers.

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Recursive Generalized Total Least Squares with Noise Covariance Estimation

Cited by 23 publications

References 19 publications

Detection of operating mode changes, without a priori model and in uncertain environments

Detection of operating mode changes, without a priori model and in uncertain environments

Automated Longitudinal Control Based on Nonlinear Recursive B-Spline Approximation for Battery Electric Vehicles

Vehicle sideslip angle estimation for a four-wheel-independent-drive electric vehicle based on a hybrid estimator and a moving polynomial Kalman smoother

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