Adaptive Blocking Matrix Filter Initialization Using Direction of Arrival
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Solution Overview
Problem
Adaptive beamformers employing adaptive blocking matrices face slow and inaccurate responsiveness in reducing noise and interference, particularly when noise levels are high relative to the desired signal, due to the complexity of modeling and adaptive nature of the filters.
Innovation Solution
The implementation of an adaptive beamforming array that initializes the adaptive filter with a computed initialization response based on the direction of arrival and inter-sensor noise correlation, using noise reference subtraction and pre-whitening techniques to enhance convergence and noise reduction, allowing for improved noise cancellation in both small and multi-sensor systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adaptive filtering is used to model noise and interference in the blocking matrix, then noise reduction capability is improved, but convergence speed and responsiveness deteriorate
Solution Approach 1:
The patent applies preliminary action by initializing the adaptive filter coefficients with pre-computed values derived from the direction of arrival (DOA) of the desired signal and the noise correlation matrix. This pre-initialization provides a head start to the adaptive filtering process, enabling the blocking matrix to achieve effective noise reduction much faster than conventional approaches that start with zero or random initialization. The pre-computed initialization values are calculated based on the statistical properties of the noise and the geometry of the sensor array, allowing the system to converge to an optimal solution in fewer iterations.
2Reliability
If adaptive filtering is used to model noise and interference in the blocking matrix, then noise reduction capability is improved, but modeling accuracy deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the initialization parameters of the adaptive filter based on the direction of arrival (DOA) of the desired signal and the noise correlation characteristics. By changing the initialization parameters according to the specific acoustic environment and signal geometry, the system achieves more accurate modeling of the noise and interference. The initialization values are computed using the DOA information and noise correlation matrix, which allows the adaptive filter to start from a more accurate estimate of the true filter coefficients, thereby improving modeling accuracy while maintaining noise reduction effectiveness.
3Reliability
If complex adaptive modeling is used in the blocking matrix, then noise reduction capability is improved, but computational complexity increases
Solution Approach 1:
The patent reduces computational complexity by performing complex calculations in advance. The initialization values are pre-computed using the direction of arrival (DOA) and noise correlation matrix before the adaptive filtering process begins. This preliminary computation eliminates the need for extensive iterative optimization during real-time operation, significantly reducing the computational burden on the processing system while maintaining the high noise reduction capability provided by the adaptive blocking matrix.
4Reliability
If adaptive filtering is used in high noise environments, then noise reduction capability is improved, but responsiveness to changes deteriorates
Solution Approach 1:
The patent improves responsiveness to changes in high noise environments by dynamically changing the initialization parameters based on updated direction of arrival (DOA) estimates and noise correlation measurements. When the acoustic environment changes or new signals arrive, the system re-computes the initialization values using the updated parameters, allowing the adaptive filter to quickly adapt to new conditions. This parameter adaptation mechanism enables the blocking matrix to maintain high noise reduction capability while being highly responsive to environmental changes, even in challenging high noise scenarios.
Data Source
AI summary
An adaptive beam-forming array uses multiple sensors and noise reference subtraction to reduce noise at an output of the adaptive beam-forming array. A direction of arrival of energy from a desired source is determined and an inter-sensor noise correlation between one or more pairs of sensors is determined. An Adaptive Blocking Matrix (ABM) generates a noise reference from an inter-sensor model representing a relationship between desired signal components received from the desired source and that are present in signals from one or more pairs of sensors. The noise reference is generated with an adaptive filter that filters a first signal from a first sensor in the pairs of sensors and is combined with the second signal from a second sensor in the pairs of sensors to produce the noise reference. The adaptive filter is initialized with an initialization response computed from the direction of arrival and the inter-sensor noise correlation.