Micromechanical Sensor Signal Cross-Correlation for Small Movement Detection
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Solution Overview
Problem
Micromechanical sensors, such as acceleration and yaw rate sensors, face challenges in accurately detecting small movements and interference effects due to aging and temperature changes, which are not adequately addressed by current calibration methods that rely on data fusion and variance analysis, leading to inefficiencies in detecting very small rotational movements.
Innovation Solution
A method that evaluates sensor signal values with different sampling intervals to detect modulation of physical variables by determining characteristic values and cross-correlation coefficients, allowing for the identification of interference effects and small movements with reduced data and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data fusion of various sensor output signals is used to correct erroneous sensor outputs, then sensor accuracy under aging and temperature changes is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent extracts and analyzes only the essential characteristic of sensor signals related to small movements and interference effects, rather than fusing all sensor data. By focusing on specific signal characteristics and using cross-correlation analysis, the method achieves correction of erroneous outputs without requiring complex multi-sensor fusion algorithms.
Solution Approach 2:
The patent replaces complex computational data fusion mechanisms with a simplified signal processing approach based on cross-correlation analysis. This substitution reduces computational requirements while maintaining the ability to detect and correct sensor errors caused by aging and temperature changes.
2Measurement precision
If variance analysis is used to detect interference effects, then detection capability is improved, but data requirements and processing time increase
Solution Approach 1:
The patent applies partial action by using cross-correlation analysis on selected signal characteristics rather than performing comprehensive variance analysis on all sensor data. This approach achieves sufficient detection capability for small movements and interference effects while significantly reducing processing time and data requirements.
3Ease of manufacture
If current calibration methods are used for micromechanical sensors, then manufacturing process is simplified, but detection of very small rotational movements becomes unreliable
Solution Approach 1:
The patent enables sensors to perform self-calibration by analyzing their own output signals for characteristic patterns of small movements and interference effects. This self-service approach maintains manufacturing simplicity while improving detection reliability, as the sensors automatically compensate for their own errors without requiring complex external calibration equipment or procedures.
Data Source
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AI summary
The invention relates to a method (800) for detecting a modulation of a physical quantity (a), wherein a plurality of sensor signal values (115) of the physical quantity (a) detected at a specific time (ti) are read in (810). Furthermore, a characteristic value (m) representing a characteristic curve (300) determined from at least two of the sensor signal values (115) is generated (820), and the modulation of the physical quantity (a) is identified (830) when the characteristic value (m) is in a predetermined ratio to a threshold value. Alternatively, first sensor signal values (115a) and second sensor signal values (115b) of the physical quantity (a) detected at specific time (ti) are stored in a first (150) and second memory (155) (840), wherein the first sensor signal values (115a) were detected with a larger sampling interval than the second sensor signal values (115b).Furthermore, a cross-correlation between the first and second sensor signal values (115) is determined (850) to establish a decision parameter (A). The modulation of the physical quantity (a) is then determined from the relationship of the decision parameter (A) to a detection threshold (Astat) (860).