Method for measuring yaw velocity of vehicle by filtering based on wavelet transformation

Abstract

The invention discloses a method for measuring the yaw velocity of a vehicle by filtering based on wavelet transformation, which is suitable for measuring the yaw velocity of an automobile of which front wheels are steering wheels and back wheels are non-steering wheels. The method comprises the following steps of: firstly, measuring the wheel speeds of the two non-steering wheels, removing a gross wheel speed error with a median filtering method and further calculating the non-filtered yaw velocity of the vehicle according to the motion relation of planar composite motion and wheel speeds subjected to median filtering; secondly, performing seven-layer wavelet decomposition on the non-filtered yaw velocity of the vehicle by using a 'sym8' wavelet basis according to a wavelet transformation theory and performing threshold value quantification on a high-frequency coefficient with a soft-threshold method; and lastly, performing reverse wavelet transformation by using a seventh layer low-frequency coefficient and the high-frequency coefficient which is subjected to threshold value quantification and reconstructing to obtain high-accuracy filtered yaw velocity.

Description

technical field [0001] The present invention relates to a filter measurement method of vehicle yaw rate based on 'sym8' wavelet, the purpose of which is to achieve high-precision measurement of yaw rate through low-cost wheel speed sensors, so as to evaluate the handling stability of vehicles, belonging to Automotive road test and testing field. Background technique [0002] Handling stability is an important evaluation index of vehicle active safety and one of the important performances of modern vehicles. The vehicle yaw rate is an important symbol to measure the vehicle handling stability. It is an important parameter in fields such as vehicle handling test and ESP electronic stability program. In GB / T6326-1994 "Automobile Handling Stability Test Method" and QC / T480-1999 "Automobile Handling Stability Index Limits and Evaluation Methods" have made clear provisions, a number of basic evaluation contents of automobile handling stability Take the yaw rate as an important ...

AI Technical Summary

Problems solved by technology

Due to the strong randomness of the interference signal and the complex frequency components, when the frequency bands of the signal and noise overlap, the method of performing time-frequency transformation on the signal by Fourier transform cannot effectively distinguish it in the frequency domain, and cannot filter it well. Eliminate the influence of interfering signals

Kalman filtering needs to establish an accurate system state and observation model, and needs to know accurate system noise and observation noise, which are difficult to obtain accurately in actual operation, resulting in low accuracy of Kalman filtering

Neural network filtering requires more prior data and longer training time, which restricts the application of neural network filtering

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