Strathprints logo
Strathprints Home | Open Access | Browse | Search | User area | Copyright | Help | Library Home | SUPrimo

Improving the performance of GA-ML DOA estimator with a resampling scheme

Li, M. and Lu, Y. (2004) Improving the performance of GA-ML DOA estimator with a resampling scheme. Signal Processing, 84 (10). pp. 1813-1822. ISSN 0165-1684

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

The maximum likelihood (ML) direction of arrival (DOA) estimator computed by genetic algorithm (GA) for the exact global solution gives a superior performance compared to other methods. In this paper, we present a resampling-based scheme to improve its ability to resolve closely spaced sources, and to enhance its global convergence. For this purpose, multiple GA–ML estimators are constructed in a parallel manner based on resampling of a single data set, then those estimates are involved into a competition, and successful results are selected and combined to generate a more accurate estimate. Numerical studies demonstrate that the proposed scheme provides less DOA estimation root-mean-squared error (RMSE), higher source resolution probability and lower resolution threshold signal-to-noise ratio (SNR) than some popular approaches including GA–ML; and this technique is not sensitive to the array geometry.

Item type: Article
ID code: 38427
Keywords: GA–ML DOA estimator , resampling scheme , genetic algorithm, Electrical engineering. Electronics Nuclear engineering, Signal Processing, Software, Computer Vision and Pattern Recognition, Control and Systems Engineering, Electrical and Electronic Engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 12 Mar 2012 16:09
    Last modified: 04 Sep 2014 21:50
    URI: http://strathprints.strath.ac.uk/id/eprint/38427

    Actions (login required)

    View Item