Vehicle State and Bias Estimation Based on Unscented Kalman Filter with Vehicle Hybrid Kinematics and Dynamics Models

In recent years,vehicle state estimation methods incorporating different vehicle models have received extensive attention.When the vehicle is disturbed by external forces not considered in traditional vehicle models(for example,a certain slope,or wind resistance different from theoretical calculation),the problem of model mismatch will occur,which leads to the inaccurate estimation of the vehicle states.To solve this problem,an Unscented Kalman Filter(UKF)algorithm is used to fuse inertial navigation data with the vehicle hybrid model in this paper.The hybrid model introduces a switching strategy that fuses the vehicle kinematics and the dynamics models while augmenting biases that need to be estimated in the vehicle states.The switching strategy resolves the integration divergence problem of vehicle dynamics models at low speeds and the inaccurate estimation of vehicle kinematics models at high speeds.Simulation experiments demonstrate that the proposed method can accurately estimate biases induced by external forces,enhancing the accuracy and confidence of states by eliminating errors caused by these biases.The robustness of the method is validated in vehicle verification platform experiments,where errors in vehicle lateral speed and yaw rate are reduced by 9.7 cm/s and 0.012°/s,respectively,under large curvature maneuvers,and 9.6 cm/s and 0.004°/s under quarter-turn maneuvers.The proposed method significantly improves lateral speed and vehicle position accuracies.

Numerical Implementation of High-Order Vold-Kalman Filter Using Python Arbitrary-Precision Arithmetic Library

The Vold-Kalman(VK)order tracking filter plays a vital role in the order analysis of noise in various fields.However,owing to the limited accuracy of double-precision floating-point data type,the order of the filter cannot be too high.This problem of accuracy makes it impossible for the filter to use a smaller bandwidth,meaning that the extracted order signal has greater noise.In this paper,the Python mpmath arbitrary-precision floating-point arithmetic library is used to implement a high-order VK filter.Based on this library,a filter with arbitrary bandwidth and arbitrary difference order can be implemented whenever necessary.Using the proposed algorithm,a narrower transition band and a flatter passband can be obtained,a good filtering effect can still be obtained when the sampling rate of the speed signal is far lower than that of the measured signal,and it is possible to extract narrowband signals from signals with large bandwidth.Test cases adopted in this paper show that the proposed algorithm has better filtering effect,better frequency selectivity,and stronger anti-interference ability compared with double-precision data type algorithm.