Tire Patch Detection via Cross-Correlated Sensor Signals
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Solution Overview
Problem
Tire-mounted sensors face challenges in extracting information due to noise and residual oscillations in the signal, making it difficult to accurately determine properties like load and rotation speed from the sensor data.
Innovation Solution
A detection system that cross-correlates sampled data from a sensor module with a signed reference sequence, such as a signed step function, to identify perturbations induced by the tire's contact with the ground, thereby extracting properties like load and rotation speed while being robust against noise and offset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct processing of sampled sensor data is performed, then processing speed is maintained, but noise and residual oscillations significantly complicate information extraction
Solution Approach 1:
The patent applies preliminary action by performing cross-correlation processing on the sampled data before final analysis. The cross-correlation with a reference sequence is conducted in advance to enhance the signal characteristics and suppress noise, making subsequent information extraction more accurate and reliable.
2Measurement precision
If complex mathematical operations are used for signal processing, then measurement precision improves, but power consumption increases
Solution Approach 1:
The patent extracts only the essential correlation information needed for detection by cross-correlating with a reference sequence. This selective extraction approach focuses computational effort on the most relevant signal features, reducing unnecessary calculations and thereby lowering power consumption while maintaining detection accuracy.
3Adaptability or versatility
If signal offset is present in sampled data, then measurement range is maintained, but detection of features of interest becomes difficult
Solution Approach 1:
The patent introduces a reference sequence as an intermediary element in the cross-correlation process. This reference sequence acts as a mediator that helps distinguish the actual signal features from the offset by providing a known pattern to correlate against, thereby making feature detection feasible even in the presence of signal offset.
Data Source
AI summary
A detection system for extracting information from a sensor module in a rolling wheel comprises: a sensor module which comprises a sensor adapted for sensing a physical property of the tire when mounted in a tire of the wheel or on an inner surface of a tire of the wheel; an acquisition module adapted for sampling a signal from the sensor module, thus obtaining a sequence of data samples; a correlation module adapted for cross-correlating a signed reference sequence with the sequence of data samples thus obtaining a correlation sequence; an extraction module adapted for identifying at least one perturbation in the correlation sequence, wherein the perturbation is induced when a part of the tire where the sensor is mounted hits the ground thereby forming the tire patch.