Abstract
In this paper, a novel algorithm with high computational efficiency is proposed for the filter adaptation in a feedforward active noise control system. The proposed algorithm Zero Forcing Block Adaptive Filter (ZF-BAF) performs filter adaptation on a block-by-block basis in the frequency domain. Filtering is performed in the time domain on a sample-by-sample basis. Working in the frequency domain permits us to get sub-linear complexity, whereas filtering in the time domain minimizes the latency. Furthermore, computational burden is tunable to meet specific requirements about adaptation speed and processing load. No other parameter tuning according to the working condition is required. Computer simulations, performed in different realistic cases against other high-performing time and frequency-domain algorithms, show that achievable performances are comparable, or even better, with those of the algorithms perfectly tuned for each specific case. Robustness exhibited in the tests suggests that performances are expected to be even better in a wide range of real cases where it is impossible to know a priori how to tune the algorithms.
Highlights
Noise cancellation encompass all the techniques that are able to remove unwanted noise given a signal affected by it
Interferece (EMI) filtering [6,7] The latter, Active Noise Cancellation (ANC) is a technique that, by processing two signals, namely a reference signal and an error signal, by means of adaptive filtering, produces another signal, named antinoise, that reduces the level of an undesired noise [8,9,10]
We investigated the Zero Forcing Block Adaptive Filter (ZF-Block Adaptive Filtering algorithm (BAF)) algorithm’s adaptation speed, noise reduction, and robustness against plant noise, different kinds of primary noise and changes in secondary paths
Summary
Noise cancellation encompass all the techniques that are able to remove unwanted noise given a signal affected by it. This methodology, simple in practice, can be implemented in very complex fashions, involving sub-band adaptive filtering [3] or machine-learning-based techniques [4,5]. It is often a very convenient solution in electronic power systems for Electro-Magnetic. Interferece (EMI) filtering [6,7] The latter, Active Noise Cancellation (ANC) is a technique that, by processing two signals, namely a reference signal and an error signal, by means of adaptive filtering, produces another signal, named antinoise, that reduces the level of an undesired noise [8,9,10]. Notable examples include automotive [11,12], biomedical [13], power [14,15] and aerospace [16]
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