In order to resolve multiple closely spaced sources moving in a tight formation using unattended acoustic sensors, the array aperture must be extended using a sparse array geometry. Traditional sparse array algorithms rely on the spatial invariance property often leading to inaccurate Direction of Arrival (DOA) estimates due to the large side-lobes present in the power spectrum. Many problems of traditional sparse arrays can be alleviated by forming a sparse array using randomly distributed single microphones. The power spectrum of a random sparse array will almost always exhibit low side-lobes, thus increasing the ability of the beamforming algorithm to accurately separate and localize sources. This paper examines the robustness of randomly distributed sparse array beamforming in situations where the exact sensor location is unknown and benchmark its performance with that of traditional baseline sparse arrays. A realistic acoustic propagation model is also used to study fading effects as a function of range and its influence on the beamforming process for various sparse array configurations.
Various sparse array configurations have been studied to improve
spatial resolution for separating several closely spaced targets in
tight formations using unattended acoustic arrays. To extend the
array aperture, it is customary to employ sparse array
configurations with uniform inter-array spacing wider than the
half-wavelength intra-subarray spacing, hence achieving more
accurate direction of arrival (DOA) estimates without using extra
hardware. However, this larger inter-array positioning results in
ambiguous DOA estimates. To resolve this ambiguity, sparse arrays
with multiple invariance properties could be deployed.
Alternatively, one can design regular or random sparse array
configurations that provide frequency diversity, in which case every
subarray is designed for a particular band of frequencies. These
different configurations are investigated in this paper.
Additionally, we present a Capon DOA algorithm that exploits the
specific geometry of each array configuration. Simulation results
are presented to study the pros and cons of different sparse
configurations.
Prediction of acoustic transmission loss (TL), or the attenuation of
sound pressure level (SPL) is a complex problem dependent on a
variety of physical parameters. Prediction of the TL using a numeric
parabolic equation (PE) method is often accepted as a method of
providing accurate TL prediction, but the large computational time
is a hinderance in applications requiring real-time situation
awareness. In order to overcome these extreme computational
requirements a neural network-based environmentally adaptive TL
prediction method is proposed and developed in this paper. This
method uses multiple back-propagation neural network (BPNN)
predictors, each trained on specific environmental conditions, and
then probabilistically combines the outputs of these predictors in a
fusion center to obtain a final TL estimate. This method is
implemented on a data set generated using a PE model for a wide
range of geometric and environmental parameters. The results are
then benchmarked against a single neural network-based prediction
scheme.
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