Adaptive Wheel Speed Sensor Filtering for Noise Suppression
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Solution Overview
Problem
Wheel speed sensors onboard vehicles often produce sensor data with unwanted or extraneous variations due to noise, leading to inconsistent and inaccurate speed measurements, which can affect the performance of vehicle systems reliant on precise wheel speed representations.
Innovation Solution
An adaptive noise filtering method is employed to filter out noise from wheel speed sensors, distinguishing between steady and non-steady intervals of wheel rotation, using adaptive and non-adaptive filtering processes based on variability calculations and filter selection graphs to select appropriate low-pass filters for each wheel sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If noise filtering is applied to wheel speed sensor data, then measurement precision is improved, but device complexity increases due to the need for adaptive filtering processes and multiple filter options
Solution Approach 1:
The patent implements dynamic filtering by switching between different filter types (low-pass, median, moving average) based on real-time operating conditions such as wheel speed thresholds and variance calculations. The system adapts its filtering strategy during steady-state versus transient conditions, making the filtering process dynamic rather than static to optimize precision without excessive complexity.
Solution Approach 2:
The system changes filtering parameters dynamically by adjusting which filter algorithm is applied based on calculated variance thresholds and wheel speed conditions. When variance exceeds certain thresholds, the system switches to more aggressive filtering; when variance is low, it uses lighter filtering to preserve signal fidelity, thus managing complexity through parameter adaptation.
2Measurement precision
If adaptive filtering is used to compensate for sensor variances, then measurement precision is improved, but processing time increases due to variability calculations and filter selection processes
Solution Approach 1:
The system performs preliminary variance calculations on buffer stores of sensor data to establish baseline characteristics before full processing is required. By pre-calculating variance metrics and determining filter needs in advance, the system reduces real-time processing requirements and minimizes latency in the filtering operation.
Solution Approach 2:
The patent segments the filtering process into distinct phases: initial variance calculation phase, filter selection phase, and execution phase. This segmentation allows the system to process data in manageable chunks rather than analyzing entire data streams at once, reducing overall processing time while maintaining precision.
3Adaptability or versatility
If multiple filter options are provided for different noise levels, then adaptability is improved, but device complexity increases due to the need for filter selection graphs and cross-referencing processes
Solution Approach 1:
The system applies different filtering characteristics to different segments of the operating range. Low-pass filters with specific cutoff frequencies are selected based on local noise conditions rather than applying a uniform filter across all conditions. This local optimization allows the system to be highly adaptable to specific noise levels without requiring an overly complex global selection mechanism.
Solution Approach 2:
The patent introduces an intermediary variance calculation mechanism that mediates between raw sensor data and the final filtered output. This intermediary process calculates statistical variance and uses it to automatically select appropriate filter parameters, acting as a mediator that simplifies the overall system architecture while maintaining high adaptability to different noise conditions.
Data Source
AI summary
Noise filtering for a wheel speed or other sensor, such as a wheel speed sensor configured for sensing rotational speed of a wheel included onboard a vehicle. A filtering process may include determining sensor data generated with the wheel speed sensor, the sensor data representing rotational speed of the wheel, determining a steady interval of wheel rotation and a non-steady interval of wheel rotation, and filtering noise from the sensor data associated with the steady interval according to an adaptive filtering process.