Multi-Sensor State Prediction for Accurate Dynamic Position 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 Kalman filter operations, reducing computational complexity by non-uniform sampling and utilizing a sense signal model matrix for accurate position measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If concurrent instantaneous measurement of all sensor signals is performed to obtain accurate position information, then measurement precision is improved, but processor clock rate and computational complexity increase significantly
Solution Approach 1:
The patent pre-calculates and stores a lookup table containing pre-computed position values based on sensor signal combinations before the system operates. During runtime, the system only needs to query this pre-computed table rather than performing complex real-time calculations, thereby maintaining high measurement precision while significantly reducing processor requirements and computational complexity
Solution Approach 2:
The patent creates a simplified digital model (lookup table) that copies the essential position information from complex sensor measurements. Instead of processing the full complexity of concurrent sensor signals in real-time, the system uses the pre-computed lookup table which contains the necessary position data in a simplified, easily accessible format, reducing computational burden while preserving measurement accuracy
2Productivity
If high processor clock rates are used to handle large bandwidths and concurrent signal processing, then productivity is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent performs computationally intensive signal processing operations in advance and stores the results in a lookup table. During actual operation, the system achieves high productivity by simply querying pre-computed values rather than performing real-time calculations, thereby maintaining fast response times while using simple, low-cost processors with low clock rates
3Ease of manufacture
If non-ideal arrangement of position indicator and sensor chip is used, then ease of manufacture is improved, but measurement precision deteriorates due to signal distortions
Solution Approach 1:
The patent uses multiple sensors to provide redundant measurements and implements a feedback mechanism where the system evaluates sensor signal quality and selects the most reliable signals for position determination. This feedback approach compensates for non-ideal arrangements and manufacturing tolerances, maintaining high measurement precision even when sensors are not perfectly positioned
Solution Approach 2:
The patent changes the evaluation parameters by using a lookup table that contains pre-computed position information for various sensor signal combinations. Instead of relying on perfect sensor arrangements, the system adapts to non-ideal conditions by selecting the best available signal combinations from the lookup table, thereby maintaining measurement accuracy despite manufacturing variations
4Adaptability or versatility
If external fields such as earth's magnetic field are present, then adaptability is improved, but measurement precision deteriorates due to additional field components
Solution Approach 1:
The patent implements a feedback mechanism where multiple sensors detect external field components and the system evaluates these signals to distinguish between useful position information and external field interference. By continuously monitoring sensor outputs and selecting signals that best represent the position indicator, the system maintains measurement precision in the presence of external fields while adapting to different environmental conditions
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves high operational robustness, long-term stability, and accurate measurements with reduced computational effort, improving flexibility and cost-effectiveness in dynamic environments.
Implementation Method 1
Possible implementations include e.g. the measurement of magnetic field components of a magnetic induction field of a permanent magnet at suitable locations in space
Data Source
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AI summary
The invention describes a sensor system (1,10) for measuring a process value (ϕ(t)) of a physical system (2), comprising: a plurality of sensors (HEi), wherein each sensor (HEi) is configured to generate a sense signal (qi(t)) as a function of the process value (ϕ(t)) at a given time (tk, ti,k); a system state corrector (3,11) configured to determine an actual system state (xk|k) of the physical system (2) at a given state update cycle (k), wherein the system state (xk|k) comprises the process value (ϕk|k) at the given state update cycle (k) and at least a first order derivative (ωk|k) of the process value (ϕ(t)) at the given state update cycle (k); a system state predictor (4,12) configured to determine a predicted system state (xk|k-1) of the physical system (2) at a given prediction cycle (k,{i,k}) from a previous system state (xk-1|k-1) at a previous state update cycle (k-1); a sense signal predictor (5,13) configured to determine predicted sense signals q→^k|k−1q^i,k|k−1 at the given prediction cycle (k,{i,k}) from the predicted system state (xk|k-1) by applying a first operation to the predicted system state (xk|k-1) using a sense signal model (N) of the physical system (2) for predicting the sense signals q→^k|k−1q^i,k|k−1; wherein the system state corrector (3,11) is configured to determine the actual system state (xk|k) at the given state update cycle (k) by applying a second operation (K) to the predicted system state (xk|k-1) according to an error signal (yk|k-1,yi,k|k-1) representative of the difference between a set of acquired sense signals (qk, qi,k) acquired from the sense signals (qi(t)) each at the given prediction cycle (k,{i,k}) and the corresponding predicted sense signals q→^k|k−1q^i,k|k−1 for each of the acquired sense signals (qk, qi,k). The invention further describes a method (100) for measuring a process value (ϕ(t)) of a physical system (2).