Acoustical Direction Finding using a Bayesian Regularized Multilayer Perceptron Artificial Neural Networks on a Tri-Axial Velocity Sensor

dc.contributor.authorNnonyelu, Chibuzo Joseph
dc.contributor.authorZakayo, Ndiku Morris
dc.date.accessioned2022-02-08T17:24:52Z
dc.date.available2022-02-08T17:24:52Z
dc.date.issued2020-01
dc.descriptionarticleen_US
dc.description.abstractA two-dimensional direction-of-arrival estimation scheme based on Bayesian-regularized (BR) Multilayer Perceptron (MLP) artificial neural network (ANN) is developed around a unit acoustic vector sensor (AVS). The AVS basically consists of three collocated and orthogonally oriented velocity sensors, hence, senses acoustic waves in the three Cartesian directions while offering portability in size and simplicity in its array manifold. It is shown that the Bayesian regularized Multilayered Perceptron neural network performs well in terms of estimation’s root-mean-square error even when tested with data of different signal-to-noise ratio (SNR) after training. This is useful as it accounts for unexpected changes of received data SNR during field operation. The proposed system is ideal for applications in mobile systems such as robots for search-and-rescue operations or soldiers in the battle field to estimate the source of a sniper fire.en_US
dc.identifier.citationVol. 10(35), Jan. 2020, PP. 4493-4501en_US
dc.identifier.issn2305-0543
dc.identifier.urihttp://repository.embuni.ac.ke/handle/embuni/3971
dc.language.isoenen_US
dc.publisherIJMECen_US
dc.subjectAcoustic direction findingen_US
dc.subjectacoustic position measurementen_US
dc.subjectacoustic signal processingen_US
dc.subjectacoustic vector sensoren_US
dc.subjectartificial neural networken_US
dc.subjectBayesian regularizationen_US
dc.subjectmultilayered perceptron.en_US
dc.titleAcoustical Direction Finding using a Bayesian Regularized Multilayer Perceptron Artificial Neural Networks on a Tri-Axial Velocity Sensoren_US
dc.typeArticleen_US
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