224 research outputs found
Grid-free compressive beamforming
The direction-of-arrival (DOA) estimation problem involves the localization
of a few sources from a limited number of observations on an array of sensors,
thus it can be formulated as a sparse signal reconstruction problem and solved
efficiently with compressive sensing (CS) to achieve high-resolution imaging.
On a discrete angular grid, the CS reconstruction degrades due to basis
mismatch when the DOAs do not coincide with the angular directions on the grid.
To overcome this limitation, a continuous formulation of the DOA problem is
employed and an optimization procedure is introduced, which promotes sparsity
on a continuous optimization variable. The DOA estimation problem with
infinitely many unknowns, i.e., source locations and amplitudes, is solved over
a few optimization variables with semidefinite programming. The grid-free CS
reconstruction provides high-resolution imaging even with non-uniform arrays,
single-snapshot data and under noisy conditions as demonstrated on experimental
towed array data.Comment: 14 pages, 8 figures, journal pape
Array signal processing robust to pointing errors
The objective of this thesis is to design computationally efficient DOA (direction-of-
arrival) estimation algorithms and beamformers robust to pointing errors, by
harnessing the antenna geometrical information and received signals. Initially,
two fast root-MUSIC-type DOA estimation algorithms are developed, which can
be applied in arbitrary arrays. Instead of computing all roots, the first proposed
iterative algorithm calculates the wanted roots only. The second IDFT-based
method obtains the DOAs by scanning a few circles in parallel and thus the
rooting is avoided. Both proposed algorithms, with less computational burden,
have the asymptotically similar performance to the extended root-MUSIC.
The second main contribution in this thesis is concerned with the matched
direction beamformer (MDB), without using the interference subspace. The manifold
vector of the desired signal is modeled as a vector lying in a known linear
subspace, but the associated linear combination vector is otherwise unknown due
to pointing errors. This vector can be found by computing the principal eigen-vector
of a certain rank-one matrix. Then a MDB is constructed which is robust
to both pointing errors and overestimation of the signal subspace dimension.
Finally, an interference cancellation beamformer robust to pointing errors
is considered. By means of vector space projections, much of the pointing error
can be eliminated. A one-step power estimation is derived by using the theory
of covariance fitting. Then an estimate-and-subtract interference canceller beamformer
is proposed, in which the power inversion problem is avoided and the
interferences can be cancelled completely
Induction Machines Fault Detection Based on Subspace Spectral Estimation
International audience—The main objective of this paper is to detect faults in induction machines using a condition monitoring architecture based on stator current measurements. Two types of fault are considered: bearing and broken rotor bars faults. The proposed architecture is based on high-resolution spectral analysis techniques also known as subspace techniques. These frequency estimation techniques allow to separate frequency components including frequencies close to the fundamental one. These frequencies correspond to fault sensitive frequencies. Once frequencies are estimated, their corresponding amplitudes are obtained by using the Least Squares Estimator (LSE). Then, a fault severity criterion is derived from the amplitude estimates. The proposed methods were tested using experimental stator current signals issued from two induction motors with the considered faults. The experimental results show that the proposed architecture has the ability to efficiently and cost-effectively detect faults and identify their severity
- …