Gyroscope Bias Estimation Using Temperature-Dependent Histograms
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Solution Overview
Problem
Gyroscopes in electronic devices face challenges in accurately determining bias, which can lead to erroneous interpretations of rotation data due to temperature variations affecting each sensing axis differently, resulting in varying bias levels over time.
Innovation Solution
A processor-implemented method and device that maintain a plurality of bias estimators, including short-term and long-term estimators, associated with specific temperatures to determine the bias for each axis of the gyroscope based on temperature readings, using a histogram to represent past gyroscope readings and adapt bin widths for precise bias calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single bias estimator is used for the gyroscope, then the device complexity is low, but the measurement precision deteriorates due to temperature variations affecting different sensing axes differently
Solution Approach 1:
The patent divides the single bias estimation function into multiple specialized bias estimators, each dedicated to a specific sensing axis (X, Y, Z axes). Each estimator independently tracks bias for its assigned axis, allowing temperature-specific compensation without requiring a complex unified model. This segmentation resolves the contradiction by improving measurement precision through axis-specific tracking while keeping each individual estimator relatively simple.
Solution Approach 2:
The patent applies local quality by maintaining separate bias estimators for each sensing axis rather than using a single global estimator. Each estimator is optimized for its specific axis characteristics and temperature response, enabling localized precision improvement. The system selectively applies the appropriate estimator based on which axis is currently being measured, achieving high precision without requiring all estimators to be simultaneously active.
2Measurement precision
If multiple bias estimators are maintained for different temperatures, then the measurement precision improves, but the use of energy increases due to continuous temperature monitoring and estimator maintenance
Solution Approach 1:
The patent implements preliminary action by pre-establishing multiple temperature-specific bias estimators during device operation or calibration phases. These estimators are prepared in advance for different temperature conditions, allowing the system to quickly select and apply the appropriate estimator when temperature changes occur, rather than calculating new estimators in real-time. This reduces computational energy consumption during actual measurement operations.
Solution Approach 2:
The system continuously monitors temperature and uses this feedback to dynamically select and update the appropriate bias estimator. The temperature sensor provides real-time feedback that triggers estimator selection or updates only when temperature crosses thresholds or changes significantly, rather than continuously maintaining all estimators at full computational cost. This feedback mechanism optimizes energy usage by activating computational resources only when necessary.
3Reliability
If short-term and long-term bias estimators are maintained, then the reliability of bias determination improves, but the device complexity increases due to the need to manage multiple estimator types
Solution Approach 1:
The patent implements dynamics by making the bias estimator selection adaptive and time-dependent. The system dynamically switches between short-term estimators (for rapid, recent temperature changes) and long-term estimators (for stable, historical temperature patterns) based on current operating conditions. This dynamic adaptation allows the system to maintain high reliability by selecting the most appropriate estimator type for the current situation, while the automated selection logic keeps management complexity relatively low.
Solution Approach 2:
The patent introduces an intermediary mechanism that manages the transition and selection between short-term and long-term estimators. This intermediary layer handles the complexity of coordinator logic, determining when to use which estimator type based on temperature stability, time elapsed, and other factors. By centralizing this coordination function, the system achieves high reliability through diverse estimator options while containing management complexity within a dedicated selection module rather than distributing it throughout the entire system.
Data Source
AI summary
In one aspect, the present disclosure provides a processor-implemented method of determining a bias for an axis of a gyroscope. The method includes: obtaining a temperature reading; maintaining a plurality of bias estimators for the axis, each bias estimator associated with a temperature and configured to estimate a bias at the associated temperature, the plurality of bias estimators including a number of short term bias estimators for estimating biases for recently obtained temperatures and a number of long term bias estimators for estimating biases for temperatures obtained over a comparatively longer period of time; and determining a bias for the axis of the gyroscope based on the temperature reading and one or more of the bias estimators.