Position Sensor Pattern Localization for Multi-Target Precision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing position sensors and measuring devices face challenges in achieving high-resolution, reliable, and adaptable target object localization, particularly in environments prone to mechanical disturbances, with limitations in multi-target recognition and sensitivity to shock and vibration.
Innovation Solution
The method employs complex cross-correlation waveforms transformed into envelope and phase waveforms using machine learning, enabling precise position recognition through correlation observations, and utilizes FIR filters for real-time evaluation, allowing for high-resolution and robust target object identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time measurement methods are used for position determination, then the measurement process is simple, but the resolution and reliability of target localization are insufficient
Solution Approach 1:
The patent applies preliminary action by generating complex cross-correlation waveforms in advance and transforming them into envelope and phase waveforms before actual position measurement. This preprocessing enables higher resolution position determination without increasing the complexity of the real-time measurement process, as the complex signal processing is performed beforehand and stored for subsequent correlation operations.
Solution Approach 2:
The patent transitions from traditional one-dimensional time measurement to two-dimensional analysis by introducing both envelope waveform and phase waveform dimensions. This dimensional expansion allows simultaneous extraction of multiple position parameters from the same measurement signal, improving localization resolution while distributing the processing complexity across different signal dimensions rather than increasing temporal sampling requirements.
2Measurement precision
If high A/D conversion rates are used to improve measurement resolution, then position determination precision increases, but device complexity and cost increase
Solution Approach 1:
The patent changes the parameter domain by transforming the measurement approach from direct time-domain sampling at high rates to frequency-domain analysis using cross-correlation of envelope and phase waveforms. This parameter transformation enables achieving high position measurement resolution through waveform transformation and correlation analysis rather than through high-rate A/D conversion, thereby reducing the required conversion rate and associated device complexity.
3Adaptability or versatility
If simple time measurement is used, then the device is less sensitive to mechanical disturbances, but multi-target recognition and adaptability are limited
Solution Approach 1:
The patent applies preliminary action by pre-generating complex cross-correlation waveforms for multiple potential target positions and transforming them into envelope and phase waveforms before actual measurement. This allows the system to rapidly compare incoming signals against multiple pre-prepared reference waveforms, enabling multi-target recognition without requiring complex real-time processing that would amplify sensitivity to mechanical disturbances during measurement.
Solution Approach 2:
The patent uses copying by creating multiple reference envelope and phase waveforms corresponding to different target positions and configurations. These reference waveforms are stored and used for comparison with actual measurement signals, enabling the system to recognize multiple targets by matching against copied reference patterns rather than requiring complex real-time analysis that would be more susceptible to mechanical interference.
4Measurement precision
If complex cross-correlation waveforms are transformed into envelope and phase waveforms, then position recognition precision improves, but signal processing time increases
Solution Approach 1:
The patent applies preliminary action by performing the complex waveform transformation from cross-correlation waveforms to envelope and phase waveforms in advance, before actual position measurement is required. The transformed waveforms are stored for subsequent rapid correlation operations, thereby achieving high position recognition precision without incurring the processing time penalty during critical measurement operations.
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
This approach achieves higher resolution, reliable operation with multiple targets, reduced sensitivity to mechanical disturbances, and lower A/D conversion rates, simplifying sensor design and reducing phantom target recognition.
Implementation Method 1
A magnetostrictive position measuring device comprising one or more position sensors working by magnetostriction
Implementation Method 2
ultrasonic distance sensors or corresponding position measuring devices which typically comprise an ultrasound transmitter for transmitting an ultrasound wave packet
Data Source
AI summary
In the method and the device described here for operating an electromagnetic, in particular magnetostrictive, or acoustic position measuring device (10) having at least one position sensor (44), at least one position encoder (20) and a waveguide (14), said position measuring device having a control device for triggering an interrogation pulse in the waveguide (14) and an evaluation unit for evaluating a wave form (400-450) recorded by the position sensor (44), wherein the time between the triggering of the interrogation pulse and the point in time at which the wave form (400-450) recorded by the position sensor (44) is recorded is measured to determine the position of the position encoder (20) via the relationship between distance and time, it is in particular provided that position recognition is implemented by means of the position encoder (20) by localising patterns in the wave form (400-450) recorded by the position sensor (44), wherein the localisation of such patterns is implemented on the basis of correlation observations and wherein a cross-correlation pattern characterising the position encoder (20) is transformed into another pattern that enables autonomous position recognition of the position encoder (20) by means of machine learning.