23,187 research outputs found
Robust direction of arrival estimation in non-Gaussian noise
Cataloged from PDF version of article.In this correspondence, a nonlinearly weighted least-squares
method is developed for robust modeling of sensor array data. Weighting
functions for various observation noise scenarios are determined using
maximum likelihood estimation theory. Computational complexity of the
new method is comparable with the standard least-squares estimation
procedures. Simulation examples of direction-of-arrival estimation are
presented
Array and multichannel signal processing using nonparametric statistics
In array signal processing a group of sensors located at distinct spatial locations is deployed to measure a propagating wavefield. The multichannel output is then processed to provide information about parameters of interest. Application areas include smart antennas in communications, radar, sonar and biomedicine.
When deriving array signal processing algorithms the noise is typically modeled as a white Gaussian random process. A shortcoming of the estimation procedures derived under Gaussian assumption is that they are extremely sensitive to deviations from the assumed model, i.e. they are not robust. In real-world applications the assumption of white Gaussian noise is not always valid. Consequently, there has been a growing interest in estimation methods which work reliably in both Gaussian and non-Gaussian noise.
In this thesis, new statistical procedures for array and multichannel signal processing are developed. In the area of array signal processing, the work concentrates on high-resolution subspace-based Direction Of Arrival (DOA) estimation and estimation of the number of source signals. Robust methods for DOA estimation and estimation of the number of source signals are derived. Spatial-smoothing based extensions of the techniques to deal with coherent signals are also derived. The methods developed are based on multivariate nonparametric statistics, in particular sign and rank covariance matrices. It is shown that these statistics may be used to obtain convergent estimates of the signal and noise subspaces for a large family of symmetric noise distributions. Simulations reveal that the techniques developed exhibit near-optimal performance when the noise distribution is Gaussian and are highly reliable if the noise is non-Gaussian.
Multivariate nonparametric statistics are also applied to frequency estimation and estimation of the eigenvectors of the covariance matrix. Theoretical justification for the techniques is shown and their robust performance is illustrated in simulations.reviewe
Robust Gravitational Wave Burst Detection and Source Localization in a Network of Interferometers Using Cross Wigner Spectra
We discuss a fast cross-Wigner transform based technique for detecting
gravitational wave bursts, and estimating the direction of arrival, using a
network of (three) non co-located interferometric detectors. The performances
of the detector as a function of signal strength and source location, and the
accuracy of the direction of arrival estimation are investigated by numerical
simulations.Comment: accepted in Class. Quantum Gravit
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
Accurate angle-of-arrival measurement using particle swarm optimization
As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates
Estimation of the Number of Sources in Unbalanced Arrays via Information Theoretic Criteria
Estimating the number of sources impinging on an array of sensors is a well
known and well investigated problem. A common approach for solving this problem
is to use an information theoretic criterion, such as Minimum Description
Length (MDL) or the Akaike Information Criterion (AIC). The MDL estimator is
known to be a consistent estimator, robust against deviations from the Gaussian
assumption, and non-robust against deviations from the point source and/or
temporally or spatially white additive noise assumptions. Over the years
several alternative estimation algorithms have been proposed and tested.
Usually, these algorithms are shown, using computer simulations, to have
improved performance over the MDL estimator, and to be robust against
deviations from the assumed spatial model. Nevertheless, these robust
algorithms have high computational complexity, requiring several
multi-dimensional searches.
In this paper, motivated by real life problems, a systematic approach toward
the problem of robust estimation of the number of sources using information
theoretic criteria is taken. An MDL type estimator that is robust against
deviation from assumption of equal noise level across the array is studied. The
consistency of this estimator, even when deviations from the equal noise level
assumption occur, is proven. A novel low-complexity implementation method
avoiding the need for multi-dimensional searches is presented as well, making
this estimator a favorable choice for practical applications.Comment: To appear in the IEEE Transactions on Signal Processin
- …