Adaptive Filter Transfer-Function Phase Correction for Stability
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Solution Overview
Problem
Active noise control systems face challenges in dynamically adapting to changes in the environment, leading to instability and reduced noise cancellation efficiency, particularly in dynamic settings like vehicles where acoustic changes occur frequently.
Innovation Solution
The method involves identifying phase errors in the adaptive filter's transfer function by analyzing the trajectory of its coefficients over time, adjusting the phase to reduce these errors, and updating the filter coefficients based on the adjusted transfer function to ensure stability and effective noise cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the adaptive filter uses fixed transfer function parameters, then the system is simpler to implement, but it cannot adapt to environmental changes leading to reduced noise cancellation efficiency
Solution Approach 1:
The system automatically identifies and corrects its own phase errors through self-diagnosis of coefficient trajectories, eliminating the need for external manual tuning and achieving self-adaptation to environmental changes
Solution Approach 2:
The system uses feedback from the adaptive filter coefficient trajectories to detect phase errors and automatically adjusts the transfer function phase accordingly, creating a closed-loop control system that adapts to environmental changes
2Productivity
If the adaptive filter coefficients are frequently updated to track environmental changes, then noise cancellation efficiency improves, but system stability deteriorates
Solution Approach 1:
The system dynamically adjusts the transfer function phase based on real-time analysis of coefficient trajectories, allowing the filter to adapt to environmental changes while maintaining stability through controlled, data-driven adjustments rather than frequent arbitrary updates
3Measurement precision
If manual tuning is performed to optimize noise cancellation, then performance improves, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-tuning by automatically analyzing its own coefficient trajectories to identify phase errors and adjusting the transfer function accordingly, completely eliminating the need for manual intervention and time-consuming manual tuning processes
Solution Approach 2:
The system replaces manual tuning operations with automated computational analysis of coefficient trajectories and algorithmic phase adjustment, substituting human operators with automated signal processing methods
Data Source
AI summary
The technology described in this document can be embodied in a computer-implemented method that includes receiving, at one or more processing devices, a plurality of values representing a set of coefficients of an adaptive filter over a period of time, and identifying, by the one or more processing devices based on the plurality of values, a phase error associated with a transfer function of the adaptive filter. The method also includes adjusting, based on the identified phase error, a phase associated with the transfer function of the adaptive filter such that coefficients calculated using the adjusted transfer o function reduce the phase error. The method further includes determining a set of coefficients for the adaptive filter based on the adjusted transfer function, and programming the adaptive filter with the determined set of coefficients to enable operation of the adaptive filter.