Fast Passive Synthetic Array Parameter Estimation by Ant Colony Optimization

Da Yong Zou; Yun Shan Hou

doi:10.4028/www.scientific.net/AMR.433-440.4506

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Fast Passive Synthetic Array Parameter Estimation...

Fast Passive Synthetic Array Parameter Estimation by Ant Colony Optimization

Abstract:

The Maximum Likelihood(ML) estimator for passive synthetic arrays incurs heavy computation when search for signal azimuth and frequency at the same time. To reduce its computational complexity, we introduced Ant Colony Optimization(ACO) to work with it. A new kind of ACO technique for continuous domain featured by Gauss kernel function is used to sample the ML spectrum, which is regarded as the fitness function in the process. The resulted estimator is called Ant Colony Optimization based ML (ACO-ML). Simulations show that ACO-ML not only reduces the computational complexity greatly but also maintains the excellent performance of the original ML estimator.

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Periodical:

Advanced Materials Research (Volumes 433-440)

Pages:

4506-4511

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.4506

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Online since:

January 2012

Authors:

Da Yong Zou, Yun Shan Hou

Keywords:

Ant Colony Optimization (ACO), Azimuth, Computational Complexity, Maximum Likelihood Estimator

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