MEMS Sensor Fusion Calibration for GPS-Denied Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
GPS-denied environments impair the performance of guided munitions and similar systems, which require GPS accuracy within missions of ten to twenty minutes, and achieving navigation-grade performance with small MEMS sensors in hostile environments is a significant challenge, with ongoing research aiming to deliver such solutions in a five to ten year timeframe, but no suitable interim solution is available.
Innovation Solution
A method for calibrating a plurality of motion sensors using a temperature-controlled chamber with varied temperatures and rotation rates, where stochastic models are estimated and decomposed into autoregressive moving average sub-processes, and a Kalman filter is built to identify bias and random walk states, with sensor-fusion approaches compared to select the lowest error method, and the sensors are packaged to prevent mutual lockup and driven at different frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS is used for navigation, then navigation accuracy is improved, but the system cannot operate in GPS-denied environments
Solution Approach 1:
The patent changes the operational parameters of MEMS sensors by calibrating them across multiple temperatures and rotation rates to create comprehensive error models. This allows the system to maintain navigation accuracy in GPS-denied environments by compensating for sensor errors through mathematical models rather than relying on GPS signals.
Solution Approach 2:
The patent creates a composite navigation solution by fusing data from multiple MEMS sensors (accelerometers and gyroscopes) with different error characteristics. By combining these sensors and their respective error models, the system achieves navigation-grade performance that compensates for the limitations of individual sensors in GPS-denied environments.
2Measurement precision
If navigation-grade sensors are used, then navigation accuracy is improved, but device size and cost increase
Solution Approach 1:
The patent uses inexpensive MEMS sensors instead of expensive navigation-grade sensors, accepting that individual MEMS sensors have higher error rates. By deploying multiple low-cost sensors and using sensor fusion with error modeling, the system achieves navigation-grade performance without the size, weight, and cost of traditional navigation-grade hardware.
Solution Approach 2:
The patent merges multiple low-cost MEMS sensors into a single navigation system, combining their outputs through sensor fusion algorithms. This approach consolidates the functionality of expensive single sensors into multiple affordable sensors, reducing overall device size and cost while maintaining navigation accuracy.
3Volume of moving object
If MEMS sensors are used, then device size is reduced, but sensor error increases
Solution Approach 1:
The patent performs preliminary calibration of MEMS sensors across multiple temperatures and rotation rates before deployment. This advance characterization creates comprehensive error models that are used during operation to compensate for sensor errors, allowing the system to achieve navigation-grade accuracy from small MEMS sensors without requiring larger, more expensive hardware.
Solution Approach 2:
The patent implements feedback through error modeling and compensation algorithms that continuously correct MEMS sensor readings. By comparing sensor outputs against pre-characterized error models and applying real-time corrections through Kalman filtering and sensor fusion, the system compensates for inherent MEMS errors and maintains high navigation accuracy.
Data Source
AI summary
An ensemble of motion sensors is tested under known conditions to automatically ascertain instrument biases, which are modeled as autoregressive-moving-average (ARMA) processes in order to construct a Kalman filter. The calibration includes motion profiles, temperature profiles and vibration profiles that are operationally significant, i.e., designed by means of covariance analysis or other means to maximize, or at least improve, the observability of the calibration model's structure and coefficients relevant to the prospective application of each sensor.