MEMS Sensor Cluster for Inertial Navigation Error Compensation
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Solution Overview
Problem
MEMS-based inertial navigation systems face challenges due to the lack of stability and poor observability of micro inertial sensors, leading to systematic and random errors that complicate accurate position calculation, especially in portable applications where size, weight, and power consumption are concerns.
Innovation Solution
The implementation of clusters of micro inertial sensors, including accelerometers and gyroscopes, which sum samples to calculate equivalent vectors and apply compensation for common errors, using a computing device to stabilize measurements and correct for misalignment and external data inputs like GPS and magnetic field measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Weight of moving object
If clusters of micro inertial sensors are used to reduce size and weight, then portability is improved, but measurement stability and accuracy deteriorate
Solution Approach 1:
The navigation system divides the sensor array into multiple clusters, where each cluster contains multiple micro inertial sensors measuring the same physical quantity. By segmenting the measurement function across multiple sensors and combining their outputs, the system achieves both reduced individual sensor size and improved overall measurement stability through statistical averaging of random errors.
Solution Approach 2:
The system merges outputs from multiple micro inertial sensors within each cluster to produce a combined measurement. This combining process, which includes summing samples and calculating equivalent vectors, reduces random errors and improves measurement stability while maintaining the portability benefits of using small micro sensors instead of large mechanical or optical sensors.
2Weight of moving object
If clusters of micro inertial sensors are used to reduce size and weight, then portability is improved, but measurement precision deteriorates
Solution Approach 1:
The system segments the precision requirement across multiple sensors, where each micro sensor in the cluster contributes to the overall measurement. By dividing the measurement function among multiple sensors and combining their outputs through statistical methods, the system achieves high position calculation accuracy despite using small, less precise individual micro sensors.
Solution Approach 2:
The system merges measurements from multiple micro inertial sensors to improve precision. The combining process includes summing samples, calculating equivalent vectors, and applying compensation algorithms that leverage information from all sensors in the cluster to produce a more accurate position calculation than any single sensor could achieve alone.
3Measurement precision
If compensation algorithms are applied to correct sensor errors, then measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary characterization of sensor errors during manufacturing or initial operation, establishing error models for each sensor cluster before actual navigation use. This preliminary action includes identifying systematic errors and random error characteristics, which are then stored and applied during operation to correct measurements without requiring complex real-time computation.
Solution Approach 2:
The system implements feedback mechanisms where measurements from multiple sensors are continuously compared and combined, with error compensation applied based on observed deviations. The feedback loop uses the statistical properties of error distributions to adjust and refine position calculations, improving accuracy while maintaining manageable complexity through iterative correction rather than complex deterministic models.
Data Source
AI summary
Device and method for providing inertial indications with high accuracy using micro inertial sensors with inherent very small size and low accuracy. The device and method of the invention disclose use of the cluster of multiple micro inertial sensors to receive from the multiple sensors an equivalent single inertial indication with high accuracy based on the multiple independent indications and mathematical manipulations for averaging the plurality of single readings and for eliminating common deviations based, for example, on measurements of the deviation of the single readings.