Multi-Sensor State Prediction for Robust Process Value Measurement
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Solution Overview
Problem
Existing sensor systems for measuring dynamic physical systems, such as position indicators, face challenges with non-ideal arrangements leading to signal distortions, susceptibility to external fields, and long-term stability issues, requiring high processor clock rates and complex signal processing.
Innovation Solution
A sensor system that uses a plurality of sensors to generate sense signals, with a system state corrector and predictor to determine actual and predicted system states through a Kalman filter operation, improving robustness and stability by reducing computational complexity and susceptibility to noise and external fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If concurrent instantaneous measurement of all sensor signals is performed to obtain position information through matrix-vector multiplication, then measurement precision is improved, but device complexity and processor requirements increase significantly
Solution Approach 1:
The patent pre-calculates and stores calibration data (matrix coefficients) during a calibration phase before actual operation. This preliminary action allows the system to use simplified real-time calculations during position measurement, avoiding complex matrix operations during critical measurement moments while maintaining accuracy.
Solution Approach 2:
The measurement process is divided into distinct phases: calibration phase (performed once) and measurement phase (repeated operations). During calibration, complex computations are performed and results stored. During measurement, only simplified look-up and interpolation operations are needed, reducing real-time computational burden.
2Productivity
If high processor clock rates are used to handle large bandwidths in position sensing systems, then productivity is improved, but device complexity and power consumption increase
Solution Approach 1:
Complex calibration computations are performed in advance during an initialization phase, and the results are stored as lookup tables or pre-computed coefficients. This eliminates the need for high-speed real-time matrix operations, allowing use of lower clock rates during actual position sensing while maintaining processing capability.
3Measurement precision
If parallel analog-to-digital converters are used for concurrent uniform sampling of sensor signals, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The sampling process is segmented into calibration sampling (performed once with high precision) and operational sampling (repeated with reduced requirements). The calibration phase captures system characteristics, enabling simplified subsequent sampling operations that don't require identical high-precision parallel ADC architecture.
Data Source
AI summary
The present disclosure describes a sensor system for measuring a process value of a physical system, including: a plurality of sensors, wherein each sensor is configured to generate a sense signal as a function of the process value at a given time; a system state corrector configured to determine an actual system state of the physical system at a given state update cycle; a system state predictor configured to determine a predicted system state of the physical system at a given prediction cycle from a previous system state at a previous state update cycle; a sense signal predictor configured to determine predicted sense signals at the given prediction cycle from the predicted system state by applying a first operation to the predicted system state using a sense signal model of the physical system for predicting the sense signals.