scholarly journals Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3711
Author(s):  
Wenfei Li ◽  
Huiyun Li ◽  
Kun Xu ◽  
Zhejun Huang ◽  
Ke Li ◽  
...  
Keyword(s):  
Vehicle Dynamics ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Moment Of Inertia ◽  
Dynamic Parameters ◽  
Vehicle Dynamic ◽  
Vehicle Model ◽  
Two Stage ◽  
Second Stage ◽  
Stage Estimation

Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.

scholarly journals Food demand in Brazil: an application of Shonkwiler & Yen Two-Step estimation method

2010 ◽  
Vol 40 (1) ◽  
pp. 186-211 ◽  
Author(s):  
Alexandre Bragança Coelho ◽  
Danilo Rolim Dias de Aguiar ◽  
James S. Eales
Keyword(s):  
Functional Form ◽  
Estimation Method ◽  
Demand System ◽  
Positive Relation ◽  
Monthly Income ◽  
Second Stage ◽  
Negative Income ◽  
Using Data ◽  
Stage Estimation ◽  
Two Step Estimation

The objective of the analysis is to estimate a demand system including eighteen food products using data from a Brazilian Household Budget Survey carried out in 2002 and 2003 (POF 2002/2003). The functional form used was Quadratic Almost Ideal Demand System (QUAIDS). Estimation employs the Shonkwiler and Yen method to account for zero consumption. Results showed that purchase probabilities of staples foods were negatively related to family monthly income, while meat, milk and other products showed a positive relation. Regional, educational and urbanization variables were also important in the first stage estimation. While some of the goods had negative income coefficients, none were inferior and six of eighteen were luxuries based on second stage estimates.

Study on Shift Schedule of Automatic Transmission Based on Road Environment and Driving Intentions

2012 ◽  
Vol 538-541 ◽  
pp. 758-761
Author(s):  
Yong Dao Song ◽  
Xiu Sheng Cheng ◽  
Xi Liu ◽  
Peng Han
Keyword(s):  
Vehicle Dynamics ◽  
Automatic Transmission ◽  
Dynamic Parameters ◽  
Vehicle Dynamic ◽  
Dual Clutch Transmission ◽  
Shift Schedule

Traditional shift schedule of automatic transmission cannot meet driving requirements when vehicle is running under conditions of complex road and special driving intentions. On the basis of the analysis of vehicle dynamics, recognition methods of road environment and driving intentions were proposed according to the changes of vehicle dynamic parameters. Traditional shift schedule was rectified and simulation was carried out in vehicle equiped with dual clutch transmission. The results shown that the rectified shift schedule can meet driving requirements. This study can provide an effective shift schedule for automatic transmission.

scholarly journals Refined Two-Stage Programming Approach of Phase Unwrapping for Multi-Baseline SAR Interferograms Using the Unscented Kalman Filter

2019 ◽  
Vol 11 (2) ◽  
pp. 199 ◽  
Author(s):  
Yandong Gao ◽  
Shubi Zhang ◽  
Tao Li ◽  
Qianfu Chen ◽  
Xiang Zhang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Surface Deformation ◽  
Unscented Kalman Filter ◽  
Phase Unwrapping ◽  
Programming Approach ◽  
Interferometric Synthetic Aperture Radar ◽  
Two Stage ◽  
Second Stage ◽  
Digital Elevation ◽  
Single Baseline

Phase unwrapping (PU) represents a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (InSAR) data. Compared with single-baseline (SB) PU, multi-baseline (MB) PU can resolve the phase discontinuities caused by noise and phase layover induced by terrain undulations. However, the MB PU performance is limited primarily by its poor robustness to measurement bias and noise. To address this problem, we propose a refined 2-D MB PU method based on the two-stage programming approach (TSPA). The proposed method uses the unscented Kalman filter (UKF) to improve the performance of the second stage of the original TSPA method. Specifically, the proposed method maintains the first stage of the TSPA to estimate the range and azimuth gradients between neighbouring pixels. Then, median filtering is slightly used to reduce the effects of the noise gradients on the estimated phase gradients. Finally, the UKF model is used to unwrap the interferometric phases using an efficient quality-guided strategy based on heap-sort. This paper is the first to integrate the UKF into the TSPA framework. The proposed method is validated using bistatic and monostatic MB InSAR datasets, and the experimental results show that the proposed method is effective for MB PU problems.

Research on the Application of Semi-Parametric Estimation Method

2014 ◽  
Vol 926-930 ◽  
pp. 3212-3215
Author(s):  
Ze Fu Zhao ◽  
Yan Chun Zhao ◽  
Xing Cong Mao
Keyword(s):  
Systematic Error ◽  
Estimation Method ◽  
Parametric Model ◽  
Temperature Monitoring ◽  
Parametric Estimation ◽  
Two Stage ◽  
Stage Estimation

The semi-parametric estimation method includes not only a parametric component, but also a nonparametric component. The two-stage estimation method is analyzed in this study and the final estimation of variables is also given. The method is used to predict the data of temperature monitoring of a certain area and the result shows that the semi-parametric model takes into account of the influence of systematic error, the prediction is preferable. The semi-parametric estimation is well applied in temperature monitoring forecast and some other actual problems.

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