3,880 research outputs found
Direction of arrival estimation using robust complex Lasso
The Lasso (Least Absolute Shrinkage and Selection Operator) has been a
popular technique for simultaneous linear regression estimation and variable
selection. In this paper, we propose a new novel approach for robust Lasso that
follows the spirit of M-estimation. We define -Lasso estimates of regression
and scale as solutions to generalized zero subgradient equations. Another
unique feature of this paper is that we consider complex-valued measurements
and regression parameters, which requires careful mathematical characterization
of the problem. An explicit and efficient algorithm for computing the -Lasso
solution is proposed that has comparable computational complexity as
state-of-the-art algorithm for computing the Lasso solution. Usefulness of the
-Lasso method is illustrated for direction-of-arrival (DoA) estimation with
sensor arrays in a single snapshot case.Comment: Paper has appeared in the Proceedings of the 10th European Conference
on Antennas and Propagation (EuCAP'2016), Davos, Switzerland, April 10-15,
201
Comparative study for broadband direction of arrival estimation techniques
This paper reviews and compares three different linear algebraic signal subspace techniques for broadband direction of arrival estimation --- (i) the coherent signal subspace approach, (ii) eigenanalysis of the parameterised spatial correlation matrix, and (iii) a polynomial version of the multiple signal classification algorithm. Simulation results comparing the accuracy of these methods are presented
Array signal processing for maximum likelihood direction-of-arrival estimation
Emitter Direction-of-Arrival (DOA) estimation is a fundamental problem in a variety of applications including radar, sonar, and wireless communications. The research has received considerable attention in literature and numerous methods have been proposed. Maximum Likelihood (ML) is a nearly optimal technique producing superior estimates compared to other methods especially in unfavourable conditions, and thus is of significant practical interest. This paper discusses in details the techniques for ML DOA estimation in either white Gaussian noise or unknown noise environment. Their performances are analysed and compared, and evaluated against the theoretical lower bounds
Off-grid Direction of Arrival Estimation Using Sparse Bayesian Inference
Direction of arrival (DOA) estimation is a classical problem in signal
processing with many practical applications. Its research has recently been
advanced owing to the development of methods based on sparse signal
reconstruction. While these methods have shown advantages over conventional
ones, there are still difficulties in practical situations where true DOAs are
not on the discretized sampling grid. To deal with such an off-grid DOA
estimation problem, this paper studies an off-grid model that takes into
account effects of the off-grid DOAs and has a smaller modeling error. An
iterative algorithm is developed based on the off-grid model from a Bayesian
perspective while joint sparsity among different snapshots is exploited by
assuming a Laplace prior for signals at all snapshots. The new approach applies
to both single snapshot and multi-snapshot cases. Numerical simulations show
that the proposed algorithm has improved accuracy in terms of mean squared
estimation error. The algorithm can maintain high estimation accuracy even
under a very coarse sampling grid.Comment: To appear in the IEEE Trans. Signal Processing. This is a revised,
shortened version of version
Multiple source direction of arrival estimation using subspace pseudointensity vectors
The recently proposed subspace pseudointensity method for direction of
arrival estimation is applied in the context of Tasks 1 and 2 of the LOCATA
Challenge using the Eigenmike recordings. Specific implementation details are
described and results reported for the development dataset, for which the
ground truth source directions are available. For both single and multiple
source scenarios, the average absolute error angle is about 9 degrees.Comment: In Proceedings of the LOCATA Challenge Workshop - a satellite event
of IWAENC 2018 (arXiv:1811.08482
A Multichannel Spatial Compressed Sensing Approach for Direction of Arrival Estimation
The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-642-15995-4_57ESPRC Leadership Fellowship EP/G007144/1EPSRC Platform Grant EP/045235/1EU FET-Open Project FP7-ICT-225913\"SMALL
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