Parameter estimation of atmospheric refractivity from radar clutter using the particle swarm optimization via Lévy flight

Zhi-Hua Zhang; Zheng Sheng; Han-Qing Shi

doi:10.1117/1.JRS.9.095998

14 October 2015 Parameter estimation of atmospheric refractivity from radar clutter using the particle swarm optimization via Lévy flight

Zhi-Hua Zhang, Zheng Sheng, Han-Qing Shi

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 095998 (October 2015). https://doi.org/10.1117/1.JRS.9.095998

Abstract

Estimating refractivity profiles from radar sea clutter is a complex nonlinear optimization problem. To deal with the ill-posed difficulties, an inversion algorithm, particle swarm optimization with a Lévy flight (LPSO), was proposed to be applied in the refractivity from clutter (RFC) technique to retrieve atmospheric duct in this paper. PSO has many advantages in solving continuous optimization problems, while in its late period it has slow convergence speed and low precision. Therefore, we integrate the Lévy flights into the standard PSO algorithm to improve the precision and enhance the capability of jumping out of the local optima. To verify the feasibility and validation of the LPSO for estimating atmospheric duct parameters based on the RFC method, the synthetic and Wallops98 experimental data are implemented. Numerical experiments demonstrate that the optimal solutions obtained from the hybrid algorithm are more precise and efficient. Additionally, to test the algorithm inversion performance, the antinoise ability of LPSO is analyzed. The results indicate that the LPSO algorithm has a certain antinoise ability. Finally, according to the experiment results, it can be concluded that the LPSO algorithm can provide a more precise and efficient method for near-real-time inversion of atmospheric refractivity from radar clutter.

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Zhi-Hua Zhang, Zheng Sheng, and Han-Qing Shi "Parameter estimation of atmospheric refractivity from radar clutter using the particle swarm optimization via Lévy flight," Journal of Applied Remote Sensing 9(1), 095998 (14 October 2015). https://doi.org/10.1117/1.JRS.9.095998

Published: 14 October 2015

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