Secondary Path Eigenvalue Optimization for ANC Convergence
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Solution Overview
Problem
Active noise control systems, such as those using the FXLMS algorithm, face performance degradation due to frequency-dependent convergence and tracking issues, particularly in non-stationary noise environments like tractor cabs or helicopter cabins, where noise frequencies fluctuate, leading to less attenuation at slower converging frequencies.
Innovation Solution
The method involves modeling the secondary path of an active noise control system by optimizing eigenvalues through iterative processes, such as equalizing the magnitude of the secondary path model while maintaining phase, using techniques like Fast Fourier Transform (FFT) and genetic search algorithms to generate optimized secondary path models that minimize eigenvalue differences, thereby improving convergence and tracking performance across all frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the FXLMS algorithm is used to account for secondary path effects, then noise control accuracy is improved, but convergence speed becomes frequency-dependent and slow at certain frequencies
Solution Approach 1:
The patent applies preliminary action by pre-processing the reference signal through the secondary path model before it enters the adaptive filter. This preprocessing step prepares the signal in advance, allowing the adaptive algorithm to converge faster by working with pre-conditioned data that already accounts for the secondary path characteristics.
Solution Approach 2:
The patent transforms the secondary path model from the time domain to the frequency domain using Fourier transforms. This parameter change in the domain representation allows for more efficient computation and faster convergence by exploiting the frequency-domain properties of the secondary path, particularly for periodic noise signals.
2Device complexity
If traditional ANC algorithms are used, then computational complexity is reduced, but performance degrades in non-stationary and multi-tone noise environments
Solution Approach 1:
The patent implements feedback by continuously monitoring the error signal and using it to update the adaptive filter coefficients in real-time. This feedback mechanism allows the system to automatically adapt to changing noise characteristics in non-stationary environments, improving reliability without requiring complex manual intervention or reconfiguration.
Solution Approach 2:
The patent employs dynamic adaptation by using an adaptive filter whose coefficients automatically adjust in response to changing noise conditions. This dynamic behavior allows the system to maintain high performance in non-stationary and multi-tone noise environments while keeping the computational structure relatively simple and manageable.
3Measurement precision
If adaptive filtering is applied to track varying noise frequencies, then tracking accuracy is improved, but computational burden increases
Solution Approach 1:
The patent changes the parameter domain from time-domain adaptive filtering to frequency-domain processing using Fourier transforms. This parameter change enables more efficient computation of tracking adjustments by exploiting the periodicity and frequency structure of the noise signals, reducing computational burden while maintaining high tracking accuracy for varying frequencies.
Data Source
AI summary
Methods for modeling the secondary path of an ANC system to improve convergence and tracking during noise control operation, and their associated uses are provided. In one aspect, for example, a method for modeling a secondary path for an active noise control system is provided. Such a method may include receiving a reference signal, filtering the reference signal with an initial secondary path model to obtain a filtered reference signal, calculating an autocorrelation matrix from the filtered reference signal, and calculating a plurality of eigenvalues from the autocorrelation matrix. The method may further include calculating a maximum difference between the plurality of eigenvalues and iterating a test model to determine an optimized secondary path model having a plurality of optimized eigenvalues that have a minimized difference that is less than the maximum difference of the plurality of eigenvalues, such that the optimized secondary path model may be utilized in the active noise control system.