Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Journal of Global Optimization
DOA Estimation using fast EM and SAGE Algorithms
Signal Processing - Image and Video Coding beyond Standards
Improving the MODEX algorithm for direction estimation
Signal Processing
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Blind search for optimal Wiener equalizers using an artificial immune network model
EURASIP Journal on Applied Signal Processing
A Refined Genetic Algorithm for Accurate and Reliable DOA Estimation with a Sensor Array
Wireless Personal Communications: An International Journal
Threshold performance analysis of maximum likelihood DOA estimation
IEEE Transactions on Signal Processing
Space-alternating generalized expectation-maximization algorithm
IEEE Transactions on Signal Processing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Some lower bounds on signal parameter estimation
IEEE Transactions on Information Theory
Single tone parameter estimation from discrete-time observations
IEEE Transactions on Information Theory
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This work presents a study of the performance of populational meta-heuristics belonging to the field of natural computing when applied to the problem of direction of arrival (DOA) estimation, as well as an overview of the literature about the use of such techniques in this problem. These heuristics offer a promising alternative to the conventional approaches in DOA estimation, as they search for the global optima of the maximum likelihood (ML) function in a framework characterized by an elegant balance between global exploration and local improvement, which are interesting features in the context of multimodal optimization, to which the ML-DOA estimation problem belongs. Thus, we shall analyze whether these algorithms are capable of implementing the ML estimator, i.e., finding the global optima of the ML function. In this work, we selected three representative natural computing algorithms to perform DOA estimation: differential evolution, clonal selection algorithm, and the particle swarm. Simulation results involving different scenarios confirm that these methods can reach the performance of the ML estimator, regardless of the number of sources and/or their nature. Moreover, the number of points evaluated by such methods is quite inferior to that associated with a grid search, which gives support to their application.