Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

ARTIFICIAL NEURAL NETWORKS FOR ESTIMATING TREE VOLUME IN THE BRAZILIAN SAVANNA

ABSTRACT This paper seeks to estimate tree volumes of different species from the Brazilian savanna by using artificial neural networks and by making comparisons of results with estimates obtained from traditional volumetric equations. Data was obtained from 15 squared samples of 400 m² in an area of 29.6 ha. In each plot, breast height diameter (D) (diameter at 1.30 m from soil), total height (Ht) and commercial height (Hc) of all individuals with D equals or higher than 3.0 cm were measured. Afterwards, each tree was felled for volume measurement. Huber method was used considering measurement of stem diameters with more than 3.0 cm. Obtained data was used to train artificial neural networks (ANN) and to adjust volumetric equations to estimate total and commercial volume of trees. This study has shown that ANN and regression models are efficient for obtaining estimated volumes of trees in the Brazilian savanna. This suggests that artificial neural networks, that take into consideration species as a categorical input variable and were data trained, presented better results than those that are trained without categorical input.

Application of median-equation approach for outlier detection in geodetic networks

In geodetic measurements some outliers may occur sometimes in data sets, depending on different reasons. There are two main approaches to detect outliers as Tests for outliers (Baarda's and Pope's Tests) and robust methods (Danish method, Huber method etc.). These methods use the Least Squares Estimation (LSE). The outliers affect the LSE results, especially it smears the effects of the outliers on the good observations and sometimes wrong results may be obtained. To avoid these effects, a method that does not use LSE should be preferred. The median is a high breakdown point estimator and if it is applied for the outlier detection, reliable results can be obtained. In this study, a robust method which uses median with or as a treshould value on median residuals that are obtained from median equations is proposed. If the a priori variance of the observations is known, the reliability of the new approch is greater than the one in the case where the a priori variance is unknown.