MEMS Inertial Navigation Unit Chip Error Correction
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Solution Overview
Problem
Current inertial motion tracking devices using MEMS sensors face challenges in accurately correcting for errors, especially in high dynamic motion applications, due to noise and drift, and require complex calibration procedures that are not suitable for consumer applications, leading to inaccurate position measurements and high error accumulation.
Innovation Solution
A compact autonomous device with a Microelectromechanical System (MEMS) Inertial Navigation Unit (INU) chip that includes a gyroscope, accelerometer, magnetometer, and a digital processor programmed with an Inertial Measurement Engine (IME) and Motion Analysis and Adaptation (MAA) program, which autonomously corrects velocity, position, and acceleration errors by periodically checking velocity changes and determining no-motion conditions to prevent error accumulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex navigation filter processing (e.g., Extended Kalman Filtering) is used to correct sensor errors, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system performs preliminary calibration by detecting no-motion conditions and computing linear acceleration bias values before actual motion occurs. This pre-correction of acceleration bias eliminates the need for complex continuous filtering during motion, as the harmful acceleration errors are removed in advance during stationary periods
Solution Approach 2:
The invention extracts and removes the harmful linear acceleration bias component from the accelerometer output separately from the normal motion signal. By identifying and eliminating only the bias error during no-motion conditions, the system avoids applying complex filtering to the entire signal chain, simplifying the processing while maintaining precision
2Measurement precision
If filtering is applied to reduce sensor noise, then measurement precision improves, but response speed deteriorates due to filtering lag
Solution Approach 1:
The system performs noise filtering and bias correction during no-motion conditions before the actual motion event. By completing the filtering and correction operations in advance when the device is stationary, the system eliminates filtering lag during dynamic motion, as no filtering is applied during the actual motion period
Solution Approach 2:
The system periodically detects no-motion conditions and performs correction operations during these intervals. This periodic correction approach allows the system to maintain precision through regular calibration while ensuring that correction operations do not interfere with or lag behind actual motion events
3Measurement precision
If complex calibration procedures are implemented to correct sensor errors, then measurement precision improves, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic self-calibration by autonomously detecting no-motion conditions through its own sensors and computing correction values without external intervention. The device monitors its own acceleration and velocity signals to identify stationary periods, then automatically computes and applies bias corrections, eliminating the need for user-initiated calibration procedures
Solution Approach 2:
The system uses feedback from its own sensor outputs (acceleration and velocity signals) to detect no-motion conditions and trigger correction operations. By continuously monitoring its own performance metrics and automatically initiating calibration when appropriate conditions are detected, the system achieves precise error correction through simple autonomous operation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device achieves accurate motion tracking with repeatability of 1 mm over distances greater than two meters, providing intuitive and adaptable error correction without the need for user-activated triggers, suitable for various applications, including sports and general human motion tracking.
Implementation Method 1
gyroscope implemented on the MEMS INU chip providing accelerometer and gyroscope input related to attitude, linear acceleration, velocity and position
Implementation Method 2
accelerometer implemented on the MEMS INU chip providing accelerometer and gyroscope input related to attitude, linear acceleration, velocity and position
Data Source
AI summary
A MEMS (Microelectromechanical System) INU (Inertial Navigation Unit) chip comprises a compact autonomous device undergoing motion tracks and analyzes its definite movement relative to local Earth coordinates with optimal accuracy and repeatability of lmm over distances of greater than two meters. The MEMS INU includes an Inertial Motion Tracking Device (IMTD) comprises a gyroscope, accelerometer, magnetometer, and digital processor programmed with a general purpose Inertial Measurement Engine (IME), an application specific Motion Analysis and Adaptation (MAA) program and a low power radio. The IMTD tracks its motion with optimum accuracy using compact practical sensors that may have noise and drift by periodically and autonomously checking its velocity changes at an optimum interval, computing a linear acceleration therefrom and determining a no-motion or motion condition relative to a threshold and correcting its velocity, position and acceleration errors when there is no motion.