A Novel Adaptive State Detector-Based Post-Filtering Active Control Algorithm for Gaussian Noise Environment with Impulsive Interference

For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the p...

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Vydáno v:Applied sciences Ročník 9; číslo 6; s. 1176
Hlavní autoři: Zhu, Wenzhao, Shi, Shengguo, Luo, Lei, Sun, Jinwei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 20.03.2019
Témata:
active impulsive noise control
Control algorithms
Entropy
information entropy
Lagrange multiplier
Methods
Performance evaluation
post-adaptive filtering
Sensors
Signal processing
state detector
ISSN:2076-3417, 2076-3417
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Shrnutí:For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the performance of conventional active impulsive noise control methods. To address these problems, a novel adaptive state detector based post-filtering active control algorithm is proposed. In this work, information entropy with adaptive kernel size is first introduced into the cost function of a post-filtering algorithm to improve its tracking. To enhance the robust performance of adaptive filters when impulsive interference happens, a recursive optimal threshold selecting method is also developed and analyzed by statistical theories. Simulations show that the new method has fast tracking ability in non-impulsive noise environment and keeps robust when impulsive interference happens. It also works well for the impulsive noise of different degrees. Experiment results confirm the effectiveness of the proposed algorithm.
Bibliografie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app9061176