scholarly journals A Novel Kalman Filter Design and Analysis Method Considering Observability and Dominance Properties of Measurands Applied to Vehicle State Estimation

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4750
Author(s):  
Julian Ruggaber ◽  
Jonathan Brembeck
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Filter Design ◽  
Optimal Filter ◽  
Estimation Problem ◽  
Road Friction ◽  
Vehicle State Estimation ◽  
Dominance Properties ◽  
Selection Of

In Kalman filter design, the filter algorithm and prediction model design are the most discussed topics in research. Another fundamental but less investigated issue is the careful selection of measurands and their contribution to the estimation problem. This is often done purely on the basis of empirical values or by experiments. This paper presents a novel holistic method to design and assess Kalman filters in an automated way and to perform their analysis based on quantifiable parameters. The optimal filter parameters are computed with the help of a nonlinear optimization algorithm. To determine and analyze an optimal filter design, two novel quantitative nonlinear observability measures are presented along with a method to quantify the dominance contribution of a measurand to an estimate. As a result, different filter configurations can be specifically investigated and compared with respect to the selection of measurands and their influence on the estimation. An unscented Kalman filter algorithm is used to demonstrate the method’s capabilities to design and analyze the estimation problem parameters. For this purpose, an example of a vehicle state estimation with a focus on the tire-road friction coefficient is used, which represents a challenging problem for classical analysis and filter parameterization.

Unscented Kalman filter for vehicle state estimation

2011 ◽  
Vol 49 (9) ◽  
pp. 1497-1520 ◽  
Author(s):  
S. Antonov ◽  
A. Fehn ◽  
A. Kugi
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Vehicle State Estimation

Comparative evaluation of Kalman filters and motion models in vehicular state estimation and path prediction

2021 ◽  
pp. 1-19
Author(s):  
Lu Tao ◽  
Yousuke Watanabe ◽  
Shunya Yamada ◽  
Hiroaki Takada
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Motion Model ◽  
Affecting Factors ◽  
Comparative Performance ◽  
Motion Models ◽  
Path Prediction ◽  
Vehicle State Estimation ◽  
Model Affect

Abstract Vehicle state estimation and path prediction, which usually involve Kalman filter and motion model, are critical tasks for intelligent driving. In vehicle state estimation, the comparative performance assessment, regarding accuracy and efficiency, of the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) is rarely discussed. This paper is devoted to empirically evaluating the performance of UKF and EKF incorporating different motion models and investigating the models’ properties and the affecting factors in path prediction. Extensive real world experiments have been carried out and the results show that EKF and UKF have roughly identical accuracy in state estimation; however, EKF is faster than UKF generally; the fastest filter is about 2⋅6 times faster than the slowest. The path prediction experiments reveal that the velocity estimate and the used motion model affect path prediction; the more realistically the model reflects the vehicle's driving status, the more reliable its predictions.

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