Optical Instrument Measurement Correction Using Kalman Filters
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Solution Overview
Problem
High-precision optical instruments, such as wavelength meters and interferometers, are sensitive to environmental fluctuations, leading to measurement drift and reduced accuracy, especially in field deployments where size, weight, power consumption, and cost (SWaP-C) are concerns.
Innovation Solution
Implementing a Kalman-type filter that combines sensor data with a process model of environmental effects on the instrument, allowing real-time compensation for environmental fluctuations and reducing measurement noise, thereby enhancing precision without affecting long-term stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-precision optical instruments are used in field deployments, then measurement capability is improved, but sensitivity to environmental fluctuations causes measurement drift and reduced accuracy
Solution Approach 1:
The patent implements feedback control by continuously monitoring environmental parameters (temperature, pressure, humidity) with sensors and using this information to adjust measurements in real-time. The Kalman filter processes the sensor data and instrument measurements, feeding back corrected values that compensate for environmental effects, thereby maintaining measurement precision despite environmental fluctuations.
Solution Approach 2:
The patent introduces environmental sensors as intermediary devices that measure environmental conditions and feed this data to the Kalman filter. These sensors act as mediators between the environment and the optical instrument, providing information that allows the system to compensate for environmental effects without directly controlling the environment.
2Stability of the object's composition
If environmental control measures are implemented, then measurement stability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical environmental control systems (such as temperature control chambers, pressure regulation equipment, and humidity control mechanisms) with a computational approach using Kalman filtering. Instead of physically controlling the environment, the system uses sensors and algorithms to mathematically compensate for environmental effects, significantly reducing mechanical complexity while maintaining measurement stability.
Solution Approach 2:
The patent changes the approach from controlling physical environmental parameters (temperature, pressure, humidity) to processing measurement parameters through mathematical filtering. The Kalman filter transforms raw sensor data and instrument measurements into corrected values by changing how the data is processed rather than changing the physical environment, thereby reducing system complexity.
3Measurement precision
If environmental sensors and compensation systems are added, then measurement accuracy is improved, but SWaP-C (size, weight, power consumption, cost) increases
Solution Approach 1:
The patent employs low-cost, compact environmental sensors that can be easily integrated into the instrument. These sensors are relatively inexpensive and have small form factors, allowing the system to gain measurement accuracy benefits without significant increases in weight or cost. The approach uses affordable sensing components rather than expensive environmental control infrastructure.
Data Source
AI summary
In a general aspect, a method is presented for increasing the measurement precision of an optical instrument. The method includes determining, based on optical data and environmental data, a measured value of an optical property measured by the optical instrument. The optical instrument includes an optical path and a sensor configured to measure an environmental parameter. The method also includes determining a predicted value of the optical property based on a model representing time evolution of the optical instrument. The method additionally includes calculating an effective value of the optical property based on the measured value, the predicted value, and a Kalman gain. The Kalman gain is based on respective uncertainties in the measured and predicted values and defines a relative weighting of the measured and predicted values in the effective value.