Paper Info
Title
A multi-Gaussian component EDA with restarting applied to direction of arrival tracking
Abstract
This paper analyzes the application of a multi-population Gaussian-based estimation of distribution algorithm equipped with a restarting strategy and mutation, named MGcEDA, to the problem of estimating the Direction of Arrival (DOA) of time-varying plane waves impinging on a uniform linear array of sensors. This problem requires the minimization of a dynamic cost function which is non-linear, non-quadratic, multimodal and variant with respect to the signal-to-noise ratio. Experiments showed that MGcEDA was able to quickly respond to changes in the source features in scenarios with different levels of noise and number of signals. Moreover, MGcEDA outperforms a previously proposed approach in all considered experiments in terms of well known performance measures.
Year
DOI
Venue
2013
10.1109/CEC.2013.6557747
Evolutionary Computation
Keywords
Field
DocType
Gaussian processes,direction-of-arrival estimation,minimisation,sensor arrays,signal processing,MGcEDA,direction of arrival tracking,dynamic cost function minimization,estimation of distribution algorithm,multiGaussian component EDA,multimodal function,multipopulation Gaussian-based estimation,nonlinear function,nonquadratic function,signal-to-noise ratio,time-varying plane waves,uniform linear sensor array,Direction of Arrival estimation,Estimation of distribution algorithm,Optimization in dynamic environments
Signal processing,Mathematical optimization,Estimation of distribution algorithm,Direction of arrival,Computer science,Sensor array,Minification,Minimisation (psychology),Gaussian,Gaussian process
Conference
ISBN
Citations 
PageRank 
978-1-4799-0452-5
1
0.36
References 
Authors
5
5
Name
Order
Citations
PageRank
André Ricardo Gonçalves1166.43
Levy Boccato2455.78
Romis Attux39522.67
Fernando J. Von Zuben483181.83
Von Zuben, F.J.511412.19