Sensor Bias Estimation Using Reference Similarity Screening
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Solution Overview
Problem
Existing bias estimation methods for sensors are inaccurate when measured values are disturbed by external factors, leading to decreased accuracy in estimating constant errors (bias) in sensor measurements.
Innovation Solution
A bias estimation apparatus and method that includes a reference model builder, temporary bias generator, corrected measured value calculator, similarity calculator, similarity selector, score calculator, and estimated bias determiner, which builds a reference model, generates temporary biases, calculates similarities, selects relevant similarities, and determines estimated biases using a computer device to improve accuracy by focusing on stable measurement data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bias estimation methods are used when measured values are disturbed by external factors, then the estimation process can be performed, but the accuracy of the estimated bias value decreases
Solution Approach 1:
The patent introduces a reference model as an intermediary between the measured values and the bias estimation process. This reference model, built from reference data under stable conditions, mediates the estimation by providing a baseline for comparison. The similarity calculator uses this reference model to identify which measured values are affected by external disturbances and should be excluded from bias calculation, thereby improving estimation accuracy despite the presence of harmful factors.
Solution Approach 2:
The patent performs preliminary actions by building a reference model in advance using reference data collected under stable operating conditions. This reference model is prepared before the actual bias estimation occurs, allowing the system to have a pre-established baseline for comparison. The reference data and model are stored and ready to be used when measurement data needs to be evaluated, enabling the system to filter out disturbed measurements before bias calculation.
2Measurement precision
If all measured values are used for bias estimation, then the calculation can be performed with available data, but the accuracy decreases when some measurements are disturbed by external factors
Solution Approach 1:
The patent extracts and removes measured values that are affected by external disturbances from the set of values used for bias estimation. The similarity calculator compares each measured value against the reference model and identifies those with low similarity (indicating disturbance). These extracted disturbed values are then excluded from the bias calculation, ensuring that only clean, undisturbed measurements contribute to the final bias estimate, thereby improving accuracy.
Solution Approach 2:
The patent applies different treatment to different measured values based on their individual quality. Instead of uniformly using all measurements, the system evaluates each measured value's similarity to the reference model and selectively includes or excludes them. This local quality assessment ensures that high-quality undisturbed measurements are used while low-quality disturbed measurements are discarded, optimizing the composition of data used for bias estimation.
Data Source
AI summary
A bias estimation apparatus according to an embodiment estimates a bias included in a measured values by each sensor. The bias estimation apparatus includes a reference model builder, a temporary bias generator, a corrected measured value calculator, a similarity calculator, a similarity selector, a score calculator, and an estimated bias determiner. The reference model builder builds a reference model of the measured value packs. The temporary bias generator generates a temporary bias pack. The corrected measured value calculator calculates corrected measured value packs. The similarity calculator calculates a similarity of each corrected measured value pack. The similarity selector selects a part of the similarities according to their values from among the similarities. The score calculator calculates a score based on the selected similarities. The estimated bias determiner determines an estimated bias which is an estimated value of the bias based on the score.