Offset Estimation in Physical Quantity Measuring Devices
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Solution Overview
Problem
Existing physical quantity measuring devices face challenges in accurately estimating offsets in non-uniform geomagnetic fields, leading to unreliable calculations due to noise and varying geomagnetic conditions, especially in portable devices like smartphones and PDAs.
Innovation Solution
A physical quantity measuring device that calculates difference vectors from detected vector data and statistically estimates offsets using a predetermined evaluation formula, with a reliability determination portion assessing the reliability of estimated reference points based on various parameters to output a highly reliable offset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional offset estimation methods are used in non-uniform geomagnetic fields, then the device can operate in various environments, but the measurement precision and reliability of offset calculations deteriorate due to noise and varying geomagnetic conditions
Solution Approach 1:
The system performs preliminary actions by collecting a large number of vector physical quantity data points before offset estimation, and pre-establishes multiple candidate offset values based on different data subsets. This preliminary data collection and candidate generation enables the system to have prepared options ready for selection under varying geomagnetic conditions.
Solution Approach 2:
The offset estimation method dynamically adapts to varying geomagnetic conditions by selecting from multiple candidate offsets based on real-time reliability assessment. The system changes its behavior dynamically by evaluating the current geomagnetic environment and choosing the most appropriate offset candidate, rather than using a fixed estimation approach.
Solution Approach 3:
The system changes parameters by evaluating different candidate offsets against reliability criteria and selecting the best match. It alters the offset parameter dynamically based on environmental conditions, using statistical evaluation to determine which candidate offset provides the most reliable estimation under current geomagnetic circumstances.
2Measurement precision
If multiple candidate offsets are calculated and reliability assessment is performed, then the measurement precision and reliability improve, but the device complexity and computational load increase
Solution Approach 1:
The system applies partial action by calculating multiple candidate offsets but not all possible combinations, and performs reliability assessment on a selective basis. It generates sufficient candidate offsets to ensure accuracy without computing every possible permutation, balancing computational effort with estimation precision.
Solution Approach 2:
The reliability assessment mechanism provides feedback by evaluating candidate offsets against statistical criteria and selecting the most reliable one. This feedback loop allows the system to automatically adjust its offset selection based on performance metrics, improving precision while managing computational complexity through intelligent selection rather than exhaustive processing.
3Speed
If rapid offset estimation is performed without comprehensive reliability assessment, then the response speed improves, but the reliability and accuracy of the offset calculation deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-calculating multiple candidate offsets from collected data before runtime selection is needed. This preliminary preparation enables rapid response during operation because the computationally intensive candidate generation has already been completed, and only selection based on reliability assessment remains.
Solution Approach 2:
The system dynamically balances speed and reliability by adjusting the depth of reliability assessment based on operational needs. In time-critical situations, it can select from pre-evaluated candidates quickly, while in less time-sensitive scenarios, it performs more comprehensive reliability checks, achieving adaptive response speed without sacrificing essential accuracy.
Data Source
AI summary
It is possible to rapidly or highly accurately estimate a highly reliable offset according to situations and improve further the reliability of the estimated offset even if a measurement data is not obtained in a space in which the magnitude of a vector physical quantity to be measured is uniform. The offset included in the obtained vector physical quantity data are statistically estimated based on a predetermined evaluation formula using difference vectors. In the estimation of the offset, reliability information on a reference point is calculated based on at least one of the vector physical quantity data, the difference vectors and a plurality of estimated reference points according to a calculation parameter for calculating the reliability information on the reference point, whether or not the reference point is reliable is determined by comparing the reliability information with a determination threshold value.