Real-Time Secondary Path Magnitude Estimation for Active Noise Control
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Solution Overview
Problem
Active noise control systems face challenges in accurately modeling and adapting to dynamic changes in secondary path transfer functions, particularly in vehicles, where environmental changes such as rolling down windows or adding objects can affect acoustic environments, leading to inefficiencies and increased costs due to the need for time-consuming measurements.
Innovation Solution
A computer-implemented method and system that estimate instantaneous magnitudes and phase values of secondary path transfer functions in real-time, allowing adaptive filter coefficients to be updated based on error signals, thereby enabling self-tuning ANC systems without prior modeling of secondary paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to model secondary path transfer functions, then measurement accuracy is improved, but measurement time and system complexity increase
Solution Approach 1:
The system uses the adaptive filter's own coefficient changes to estimate secondary path characteristics. The adaptive filter processes the noise signal and its output is fed back through the secondary path to the error sensor, creating a self-contained measurement system that eliminates external measurement equipment and reduces measurement time while maintaining accuracy.
Solution Approach 2:
The patent implements feedback by using the error signal from the error sensor to continuously adjust the adaptive filter coefficients. This feedback mechanism allows the system to automatically track changes in the secondary path transfer function in real-time, eliminating the need for time-consuming manual measurements and recalibrations.
2Reliability
If adaptive filters are used to cancel noise, then noise cancellation effectiveness is improved, but convergence speed decreases when secondary path changes occur
Solution Approach 1:
The patent applies dynamics by making the adaptive filter coefficients time-variant through continuous updating based on real-time secondary path characteristics. The coefficients are adjusted dynamically in response to changing acoustic environments, allowing the filter to maintain optimal performance and converge faster when secondary path changes occur, rather than remaining static and requiring complete recalibration.
3Adaptability or versatility
If secondary path changes are detected manually, then system adaptability is improved, but operational complexity and cost increase
Solution Approach 1:
The system automatically detects and adapts to secondary path changes using its own operational data. The adaptive filter monitors its own coefficient variations and uses these to estimate changes in the secondary path, eliminating the need for external measurement devices or manual calibration procedures while maintaining full adaptability to environmental changes.
Solution Approach 2:
The patent replaces manual mechanical measurement and calibration processes with an automated electronic system. Instead of physically measuring secondary path characteristics using external equipment, the system uses electronic signal processing and adaptive filtering algorithms to automatically detect and compensate for changes, reducing operational complexity and cost.
Data Source
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AI summary
The technology described in this document can be embodied in a computer-implemented method that includes receiving a first plurality of values representing a set of current coefficients of an adaptive filter disposed in an active noise cancellation system. The method also includes computing a second plurality of values each of which represents an instantaneous difference between a current coefficient and a corresponding preceding coefficient of the adaptive filter, and estimating, based on the second plurality of values, one or more instantaneous magnitudes of a transfer function that represents an effect of a secondary path of the active noise cancellation system. The method further includes updating the first plurality of values based on estimates of the one or more instantaneous magnitudes to generate a set of updated coefficients for the adaptive filter, and programming the adaptive filter with the set of updated coefficients.