Optical Instrument Measurement Precision Using Kalman-Type Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Optical instrumentation is inherently sensitive to environmental fluctuations, leading to unacceptable drift and loss of accuracy in measurements, especially in field applications where isolating or actively controlling the environment is impractical due to size, weight, power, and cost constraints.
Innovation Solution
Implementing a Kalman-type filter that combines sensor data with a process model to compensate for environmental fluctuations in real-time, reducing noise and increasing measurement precision without significant processing overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If environmental control measures are implemented to maintain measurement accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical/environmental control systems with a computational solution. Instead of physically controlling temperature, pressure, and humidity to maintain measurement accuracy, the system uses environmental sensors to monitor these parameters and a Kalman filter to computationally compensate for their effects on the optical measurements, thereby reducing device complexity while maintaining precision
Solution Approach 2:
The patent introduces environmental sensors as intermediaries between the environment and the optical measurement system. These sensors measure environmental parameters (temperature, pressure, humidity) which then feed into the Kalman filter algorithm to compensate for environmental effects, acting as a mediator that enables measurement accuracy without direct environmental control
2Measurement precision
If environmental sensors and compensation algorithms are added to optical instruments, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex hardware-based environmental control with a software-based Kalman filter algorithm. The filter processes sensor data computationally to compensate for environmental effects, achieving high measurement precision through signal processing rather than mechanical control, thus improving precision while managing complexity through software solutions
3Stability of the object's composition
If environmental control systems are implemented, then measurement stability is improved, but size and weight of the instrument increase
Solution Approach 1:
The patent replaces heavy mechanical environmental control systems (temperature control chambers, pressure regulation systems) with lightweight electronic sensors and computational algorithms. The Kalman filter provides measurement stability through software-based compensation rather than physical environmental control, dramatically reducing instrument weight while maintaining stability
Solution Approach 2:
The system performs self-compensation for environmental effects through the Kalman filter algorithm. The instrument automatically monitors its own environmental conditions via sensors and applies real-time compensation to the measurements, eliminating the need for external environmental control infrastructure and reducing overall system weight
Data Source
Figure 1A
Figure 1B
Figure 2A
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.