Estimation of nominal direction of arrival and angular spread using an array of sensors
Signal Processing - Special issue on subspace methods, part I: array signal processing and subspace computations
Computationally Efficient Maximum Likelihood Approach to DOA Estimationof a Scattered Source
Wireless Personal Communications: An International Journal
Approximate maximum likelihood estimators for array processing inmultiplicative noise environments
IEEE Transactions on Signal Processing
Decoupled estimation of DOA and angular spread for a spatiallydistributed source
IEEE Transactions on Signal Processing
Bearing estimation for a distributed source: modeling, inherentaccuracy limitations and algorithms
IEEE Transactions on Signal Processing
Distributed source localization using ESPRIT algorithm
IEEE Transactions on Signal Processing
Efficient Subspace-Based Estimator for Localization of Multiple Incoherently Distributed Sources
IEEE Transactions on Signal Processing
Parametric localization of distributed sources
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Estimation of directions of arrival of multiple scattered sources
IEEE Transactions on Signal Processing
A generalized capon estimator for localization of multiple spread sources
IEEE Transactions on Signal Processing
Fast DOA estimation of incoherently distributed sources by novel propagator
Multidimensional Systems and Signal Processing
Low-Complexity Estimation of DOA and Angular Spread for an Incoherently Distributed Source
Wireless Personal Communications: An International Journal
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In this paper, a simplified parametric estimator for tridimensional localization of single incoherently distributed (ID) source with small angular spread is proposed. The proposed estimator firstly obtains two sample covariance matrices using the observation data of a L-shape array. And then the secondary diagonal elements of the sample covariance matrices are used to estimate the nominal azimuth and elevation of single ID source. Our technique does not involve any spectrum searching and the eigen-decomposition of the sample covariance matrix, and thus the computational burden has been significantly alleviated. Moreover, it is also a blind estimator which doesn't require any prior knowledge about the angular power density of the ID source. Numerical examples illustrate the performance of the proposed estimator.