Inertial Sensor Calibration via Angular Velocity Distribution Analysis
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Solution Overview
Problem
Inertial sensors face challenges in achieving accurate calibration due to errors caused by temperature changes, atmospheric pressure variations, vibrations, and impacts, making it cumbersome for users to perform bias correction and determine the necessity of correction.
Innovation Solution
A calibration apparatus that includes an obtaining unit for angular velocity values, a deriving unit to calculate the distribution of differences between adjacent angular velocity values, a determination unit to assess if the sensor is in a motionless state, and a correction unit to decide and apply a bias value for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motionless state determination methods are used (comparing acceleration to gravitational acceleration), then the calibration process is simple, but the correction accuracy deteriorates due to temperature changes, atmospheric pressure variations, vibrations, and impacts
Solution Approach 1:
The patent replaces the mechanical/physical approach of determining motionless state through acceleration comparison with gravitational acceleration with a statistical approach using angular velocity distribution analysis. By substituting the determination criterion from acceleration-based to angular velocity distribution-based, the system achieves more reliable motionless state detection that is less susceptible to environmental factors like temperature, pressure, vibration, and impact.
Solution Approach 2:
The patent changes the parameter used for motionless state determination from acceleration (prone to environmental interference) to angular velocity distribution characteristics (kurtosis and skewness). This parameter change allows the system to maintain high correction accuracy even in the presence of temperature changes, atmospheric pressure variations, vibrations, and impacts, as angular velocity distribution analysis is more robust to these harmful factors.
2Ease of operation
If manual bias correction is performed by users, then the calibration process is controllable, but the ease of operation deteriorates as it becomes cumbersome
Solution Approach 1:
The patent implements self-service calibration by enabling the inertial sensor system to automatically determine its own motionless state and perform bias correction without requiring user intervention. The system autonomously analyzes angular velocity distribution, determines motionless periods, and applies correction, thereby dramatically improving ease of operation while the automated processes manage the complexity internally.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors angular velocity values, analyzes their distribution characteristics, and uses this feedback to automatically determine when the sensor is in a motionless state. This closed-loop feedback approach enables automated bias correction, eliminating the need for cumbersome manual user operations while maintaining calibration accuracy.
3Extent of automation
If automated motionless state determination is implemented, then the ease of operation improves, but the device complexity increases due to distribution analysis requirements
Solution Approach 1:
The patent segments the complex task of automated calibration into distinct functional modules: an obtaining unit for collecting angular velocity values, a deriving unit for calculating distribution characteristics (kurtosis and skewness), a determination unit for assessing motionless state based on these characteristics, and a correction unit for applying bias correction. This segmentation manages device complexity by organizing the automation process into manageable, specialized components.
Solution Approach 2:
The patent introduces intermediate statistical parameters (kurtosis and skewness of angular velocity distribution) as mediators between the raw sensor data and the motionless state determination. These intermediary metrics simplify the automation process by providing quantifiable indicators that bridge the gap between complex angular velocity data and the binary motionless/non-motionless state classification, thereby managing system complexity.
Data Source
AI summary
A calibration apparatus of an inertial sensor, obtains an angular velocity value from the inertial sensor, derives a distribution of a difference between temporally adjacent angular velocity values concerning a plurality of angular velocity values obtained during a given period, and determines, based on the distribution, whether the inertial sensor is in a motionless state during the given period. Then, if it is determined that the inertial sensor is in the motionless state, the calibration apparatus decides a bias value of the inertial sensor based on the plurality of angular velocity values and corrects the obtained angular velocity value based on the bias value.