MEMS Sensor Bias Compensation via Static State Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional MicroElectroMechanical Systems (MEMS) accelerometer and gyroscope sensors in electronic devices provide low-quality measurements due to bias and drift, limiting their applications and requiring continuous external calibration systems like GPS and magnetometers.
Innovation Solution
A method that improves MEMS sensor accuracy by computing and applying compensation parameters, including bias, heuristic, and machine learning-derived inferences, to correct candidate measurements based on the device's state, orientation, and motion patterns, eliminating the need for external systems and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If MEMS sensors are used for motion sensing applications, then cost and size are reduced, but measurement accuracy deteriorates due to bias and drift
Solution Approach 1:
The system performs self-calibration by detecting static states and automatically computing bias compensation parameters without requiring external calibration equipment or systems. The sensor system serves itself to correct its own measurement errors, eliminating the need for costly external calibration infrastructure while maintaining measurement accuracy.
Solution Approach 2:
The system dynamically changes the compensation parameters based on detected device states. By identifying static states and computing bias parameters during these states, then applying compensation during motion states, the system adapts parameters to maintain accuracy across different operating conditions while using inexpensive MEMS sensors.
2Measurement precision
If external calibration systems like GPS and magnetometers are used continuously, then measurement accuracy is maintained, but power consumption increases
Solution Approach 1:
Instead of continuous calibration, the system performs calibration periodically by detecting static states and computing bias parameters only when the device is stationary. This periodic action maintains measurement accuracy while dramatically reducing power consumption compared to continuous use of external calibration systems.
Solution Approach 2:
The system uses the device's own motion sensor data to detect static states and perform self-calibration, eliminating the need for continuous operation of power-hungry external systems like GPS and magnetometers. The device serves its own calibration needs using minimal power.
3Measurement precision
If bias compensation is applied continuously, then measurement accuracy is improved, but computational overhead increases
Solution Approach 1:
The system dynamically adjusts the compensation process by first detecting the device state (static or motion). During static states, it computes bias parameters; during motion states, it applies compensation. This dynamic approach maintains accuracy while optimizing computational resources by performing different operations based on real-time conditions.
Solution Approach 2:
The system performs bias parameter computation in advance during static states before entering motion states. By preparing the compensation parameters beforehand when computation is less critical, the system reduces real-time computational overhead during motion while maintaining measurement accuracy.
Data Source
AI summary
A method of improving accuracy of measurements of at least one motion sensor included in an electronic device, including receiving a candidate measurement associated with the electronic device from the at least one motion sensor; detecting an electronic device state associated with the electronic device, the electronic device state including one from among a static state and a motion state; computing a compensation parameter based on the candidate measurement and the electronic device state; and correcting the candidate measurement based on the computed compensation parameter.