Computationally Efficient Maximum Likelihood Approach to DOA Estimationof a Scattered Source
Wireless Personal Communications: An International Journal
Low-complexity estimation of 2D DOA for coherently distributed sources
Signal Processing - Special section: Hans Wilhelm Schüßler celebrates his 75th birthday
Robust downlink power control in wireless cellular systems
EURASIP Journal on Wireless Communications and Networking - Special issue on multiuser MIMO networks
A modified COMET-EXIP method for estimating a scattered source
Signal Processing
Gaussian channel model for mobile multipath environment
EURASIP Journal on Applied Signal Processing
Decoupled estimation of 2D DOA for coherently distributed sources using 3D matrix pencil method
EURASIP Journal on Advances in Signal Processing
Review of user parameter-free robust adaptive beamforming algorithms
Digital Signal Processing
Simplified Estimation of 2D DOA for Coherently Distributed Sources
Wireless Personal Communications: An International Journal
Fast DOA estimation of incoherently distributed sources by novel propagator
Multidimensional Systems and Signal Processing
A Fast Decoupled Nominal 2-D Direction-of-Arrival Estimation for Coherently Distributed Source
Wireless Personal Communications: An International Journal
Low-Complexity Estimation of the Nominal Azimuth and Elevation for Incoherently Distributed Sources
Wireless Personal Communications: An International Journal
Low-Complexity Estimation of DOA and Angular Spread for an Incoherently Distributed Source
Wireless Personal Communications: An International Journal
A Simplified Estimator for Tridimensional Localization of Single Incoherently Distributed Source
Wireless Personal Communications: An International Journal
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Most array processing algorithms are based on the assumption that the signals are generated by point sources. This is a mathematical constraint that is not satisfied in many applications. In this paper, we consider situations where the sources are distributed in space with a parametric angular cross-correlation kernel. We propose an algorithm that estimates the parameters of this model using a generalization of the MUSIC algorithm. The method involves maximizing a cost function that depends on a matrix array manifold and the noise eigenvectors. We study two particular cases: coherent and incoherent spatial source distributions. The spatial correlation function for a uniformly distributed signal is derived. From this, we find the array gain and show that (in contrast to point sources) it does not increase linearly with the number of sources. We compare our method to the conventional (point source) MUSIC algorithm. The simulation studies show that the new method outperforms the MUSIC algorithm by reducing the estimation bias and the standard deviation for scenarios with distributed sources. It is also shown that the threshold signal-to-noise ratio required for resolving two closely spaced distributed sources is considerably smaller for the new method