Sensor Margin Calibration Using Noise Distribution Statistics
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Solution Overview
Problem
Conventional sensor devices face challenges in maintaining robust sensing functions due to noise interference, which can lead to false triggers, missed detections, and reduced accuracy, affecting their reliability and performance.
Innovation Solution
A system comprising a statistics component, a margin component, and an output component that generates statistical data, determines sensing margins, and produces indicators for sensing decisions, utilizing noise-level information and signal distribution data to minimize noise impact and enhance sensing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor devices operate without noise characterization, then device complexity is reduced, but measurement precision and reliability deteriorate due to false triggers and missed detections
Solution Approach 1:
The system performs preliminary characterization of noise and signal distributions during a calibration phase before actual sensing operations. Statistical parameters (mean, standard deviation) are pre-calculated and stored, allowing the sensor to operate with optimized thresholds without real-time computational overhead, thus improving precision without adding complexity to the operational system
Solution Approach 2:
The sensor device autonomously characterizes its own noise and signal distributions by analyzing its output signals during calibration. The device self-determines statistical parameters and uses them to set optimal sensing thresholds, eliminating the need for external calibration equipment or complex manual configuration, thereby improving measurement precision while maintaining simple device architecture
2Reliability
If sensing thresholds are set without statistical analysis, then ease of operation is improved, but reliability deteriorates due to false alarms and missed detections
Solution Approach 1:
The sensor device automatically determines optimal sensing thresholds by analyzing its own signal and noise distributions during calibration. The system self-calibrates by calculating statistical parameters and setting thresholds that achieve target false alarm and missed detection rates, eliminating the need for manual threshold configuration and ensuring reliable operation without requiring user expertise in statistical analysis
Solution Approach 2:
The system uses feedback from signal distribution analysis to automatically adjust and optimize sensing thresholds. By monitoring the statistical characteristics of sensor outputs and comparing them against performance criteria (false alarm rate, missed detection rate), the system iteratively refines threshold settings to achieve optimal reliability without manual intervention
3Reliability
If noise impact is not minimized, then device complexity is reduced, but measurement precision and reliability worsen due to false triggers and reduced accuracy
Solution Approach 1:
The system performs preliminary noise characterization during calibration by analyzing signal distributions and calculating statistical parameters. Optimal sensing thresholds are pre-determined based on this noise analysis, allowing the sensor to minimize false triggers through statistically optimized thresholds without requiring complex real-time noise filtering or signal processing during operation
Solution Approach 2:
The system changes the operational parameters (sensing thresholds) based on statistically analyzed noise and signal characteristics. By adjusting thresholds as a function of the measured distribution parameters (mean, standard deviation), the system adapts to noise conditions and minimizes false triggers while maintaining simple processing logic that does not require complex algorithms
Data Source
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AI summary
Systems and techniques for determining sensing margins and/or diagnostic information associated with a sensor are presented. A statistics component generates statistical data based on sensor data associated with a sensing device. A margin component generates sensing margins for the sensing device based on the statistical data. An output component generates an indicator for a changing condition associated with the sensing device based on the sensing margins. In an aspect, a diagnostic component generates diagnostic data for the sensing device based on the statistical data.