9,634 research outputs found
Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks
The paper investigates the direction-of-arrival (DOA) estimation of narrow
band signals with conventional co-prime arrays by using probabilistic Bayesian
neural networks (PBNN). A super resolution DOA estimation method based on
Bayesian neural networks and a spatially overcomplete array output formulation
overcomes the pre-assumption dependencies of the model-driven DOA estimation
methods. The proposed DOA estimation method utilizes a PBNN model to capture
both data and model uncertainty. The developed PBNN model is trained to do the
mapping from the pseudo-spectrum to the super resolution spectrum. This
learning-based method enhances the generalization of untrained scenarios, and
it provides robustness to non-ideal conditions, e.g., small angle separation,
data scarcity, and imperfect arrays, etc. Simulation results demonstrate the
loss curves of the PBNN model and deterministic model. Simulations are carried
out to validate the performance of PBNN model compared to a deterministic model
of conventional neural networks (CNN).Comment: 7-page
The influence of random element displacement on DOA estimates obtained with (Khatri-Rao-)root-MUSIC
Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case
Localization of Sound Sources in a Room with One Microphone
Estimation of the location of sound sources is usually done using microphone
arrays. Such settings provide an environment where we know the difference
between the received signals among different microphones in the terms of phase
or attenuation, which enables localization of the sound sources. In our
solution we exploit the properties of the room transfer function in order to
localize a sound source inside a room with only one microphone. The shape of
the room and the position of the microphone are assumed to be known. The design
guidelines and limitations of the sensing matrix are given. Implementation is
based on the sparsity in the terms of voxels in a room that are occupied by a
source. What is especially interesting about our solution is that we provide
localization of the sound sources not only in the horizontal plane, but in the
terms of the 3D coordinates inside the room
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
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