Robust source number enumeration for r-dimensional arrays in case of brief sensor failures
2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012•ieeexplore.ieee.org
There has been much activity on model selection for multi-dimensional data in recent years
under the assumption of a Gaussian noise distribution. However, methods which are optimal
for Gaussian noise are very sensitive against brief sensor failures. We suggest two robust
model order selection schemes for multi-dimensional data based on the MM-estimator of the
covariance of the r-mode unfoldings of the complex valued data tensor. Simulation results
are given for 2-D and 3-D uniform rectangular arrays based source enumeration, both for …
under the assumption of a Gaussian noise distribution. However, methods which are optimal
for Gaussian noise are very sensitive against brief sensor failures. We suggest two robust
model order selection schemes for multi-dimensional data based on the MM-estimator of the
covariance of the r-mode unfoldings of the complex valued data tensor. Simulation results
are given for 2-D and 3-D uniform rectangular arrays based source enumeration, both for …
There has been much activity on model selection for multi-dimensional data in recent years under the assumption of a Gaussian noise distribution. However, methods which are optimal for Gaussian noise are very sensitive against brief sensor failures. We suggest two robust model order selection schemes for multi-dimensional data based on the MM-estimator of the covariance of the r-mode unfoldings of the complex valued data tensor. Simulation results are given for 2-D and 3-D uniform rectangular arrays based source enumeration, both for Gaussian noise and a brief sensor failure.
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