Sensor Noise Floor Deviation Detection in Vehicle Noise Cancellation
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Solution Overview
Problem
In vehicle-implemented noise-cancellation systems, accelerometers can malfunction, causing the noise floor to drift upward, leading to degraded performance or noise generation, necessitating real-time detection of noise floor deviations to exclude faulty sensors and prevent user intervention.
Innovation Solution
A computer-implemented method estimates the noise floor of sensors by determining power spectral densities, filtering frequency spikes, and monitoring conditions like vehicle idle state, engine RPM, and accelerator position to detect deviations from expected noise floors, incrementing/decrementing counters to manage adaptive filter updates and noise-cancellation signal production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the noise floor estimation is performed continuously using all sensor signals, then the noise cancellation performance is improved, but faulty sensors cause noise generation and degraded performance
Solution Approach 1:
The system performs preliminary detection of the noise floor using power spectral density analysis before the faulty sensor can significantly degrade performance. By continuously monitoring the sensor signals and identifying when the noise floor exceeds expected levels, the system can detect sensor malfunctions early and exclude the faulty sensor from noise cancellation calculations, preventing noise generation while maintaining reliable operation
Solution Approach 2:
The system implements feedback by comparing the estimated noise floor against expected noise floor levels and using this information to control the inclusion/exclusion of sensors in the noise cancellation process. When the noise floor is within expected ranges, sensors are included for optimal cancellation; when the noise floor exceeds thresholds, sensors are excluded to prevent noise generation, thus maintaining system reliability
2Reliability
If the sensor signal is continuously monitored for noise floor detection, then sensor malfunctions are detected in real-time, but the system complexity and computational load increase
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on specific aspects of the sensor signal that are most indicative of noise floor changes. Rather than analyzing every aspect of the sensor output continuously, the system computes power spectral densities at selected frequencies and uses threshold-based detection, achieving real-time malfunction detection with reduced computational complexity compared to full-spectrum continuous analysis
3Measurement precision
If the noise floor threshold is set low for sensitive detection, then sensor deviations are detected early, but false positives increase causing unnecessary sensor exclusion
Solution Approach 1:
The system introduces an intermediary threshold mechanism that compares the estimated noise floor against expected noise floor levels derived from multiple factors including vehicle operating conditions. Rather than using a fixed low threshold that would cause false positives, the system uses a dynamic threshold that adapts to different vehicle states (idling, driving, parked), allowing sensitive detection of actual deviations while filtering out false positives caused by normal operational variations
Data Source
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AI summary
A computer-implemented method for determining whether the noise floor of a sensor is deviated from an expected value, comprising the steps of: receiving a sensor signal from a sensor; determining a plurality of power spectral densities from a plurality of successive frames of samples of the sensor signal, each of the plurality of power spectral densities being determined from a respective frame of the plurality of successive frames, each power spectral density being comprised of a plurality of frequency bins, each frequency bin being associated with a power of the respective frame at the frequency of the respective frequency bin, wherein each successive frame of the plurality of successive frames differs by at least one sample; identifying a minimum power of the plurality of power spectral densities; and determining whether the minimum power exceeds a threshold value.