Adaptive TVD Filtering for Vehicle State Estimation
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Solution Overview
Problem
Current vehicle data filtering methods struggle to retain peak information while improving data smoothness and are not adaptive to real-time changes in signal intensity and noise levels, especially under extreme conditions.
Innovation Solution
An adaptive total variation denoising (TVD) filtering method using Teager-Kaiser energy evaluation and noise-based adaptive parameters to optimize signal filtering, ensuring peak information is retained and data smoothness is maintained, with noise reduction and vehicle state estimation applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If traditional filtering methods (low-pass, high-pass, band-pass) are used to improve data smoothness and reduce noise, then data smoothness is improved, but peak information is lost
Solution Approach 1:
The patent changes the filtering parameter from fixed traditional filter coefficients to adaptive TVD parameters that are dynamically adjusted based on local signal characteristics. By computing adaptive parameters from signal statistics (mean, variance, skewness, kurtosis) and using them in the TVD optimization framework, the filter can preserve peaks while smoothing noise, resolving the contradiction between smoothness and peak retention
Solution Approach 2:
The patent introduces dynamic adaptivity into the filtering process by continuously adjusting filter parameters based on local signal conditions. The adaptive parameters are recomputed at each iteration based on current signal statistics, allowing the filter to dynamically respond to changing signal characteristics and preserve important features like peaks while maintaining smoothness
2Object-affected harmful factors
If TVD filtering is applied to sparse vehicle data for noise reduction, then noise reduction effectiveness is improved, but adaptability to real-time signal intensity and noise level changes is reduced
Solution Approach 1:
The patent implements feedback mechanisms where signal statistics (mean, variance, skewness, kurtosis) are continuously computed from the input signal and fed back to adjust the TVD filter parameters. This closed-loop approach allows the filter to automatically adapt to real-time changes in signal intensity and noise levels, improving both adaptability and noise reduction effectiveness
Solution Approach 2:
The patent dynamically changes filter parameters based on computed signal statistics. The adaptive parameters are derived from local signal characteristics and used to adjust the TVD optimization problem, enabling the filter to adapt to varying signal conditions and improve noise reduction effectiveness across different working conditions
3Device complexity
If fixed parameter filtering is used for vehicle data, then device complexity is reduced, but filtering effectiveness under extreme working conditions is worsened
Solution Approach 1:
The patent introduces adaptive parameter computation that calculates filter parameters based on signal statistics (mean, variance, skewness, kurtosis) and local signal characteristics. This allows the filter to automatically adjust to extreme working conditions without requiring complex manual tuning, improving reliability while maintaining reasonable computational complexity through efficient statistical computations
Data Source
AI summary
A vehicle state estimation method based on adaptive total variation denoising (TVD) filtering includes the following steps: step 1: collection and preprocessing of an original signal of a vehicle; step 2: noise level evaluation; step 3: Teager-Kaiser energy evaluation; step 4: optimization problem construction; and step 5: application of a filtered signal in the step 4 in the estimation of a vehicle state. The vehicle state estimation method is mainly based on the global noise level characteristic and the local intensity change characteristic of the vehicle system state data, and adaptive filtering of parameters is achieved by means of a TVD filtering method. The signal is denoised to the maximum extent, peak information of the signal is retained while the data smoothness is maintained, and then the signal is used for vehicle state estimation, working condition identification and the like.