Acoustical Direction Finding using a Bayesian Regularized Multilayer Perceptron Artificial Neural Networks on a Tri-Axial Velocity Sensor
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Date
2020-01
Journal Title
Journal ISSN
Volume Title
Publisher
IJMEC
Abstract
A 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.
Description
article
Keywords
Acoustic direction finding, acoustic position measurement, acoustic signal processing, acoustic vector sensor, artificial neural network, Bayesian regularization, multilayered perceptron.
Citation
Vol. 10(35), Jan. 2020, PP. 4493-4501