Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-25T16:46:57.548Z Has data issue: false hasContentIssue false
  • English
  • Français

Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm

Published online by Cambridge University Press:  21 February 2018

Chao Yang  and
Lixin Guo
Chao Yang*
Affiliation:
School of Science, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, China
Lixin Guo
Affiliation:
School of Physics and Optoelectronic Engineering, Xidian University, Xi'an, Shaanxi, China
*
Author for correspondence: Chao Yang, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, an orthogonal crossover artificial bee colony (OCABC) algorithm based on orthogonal experimental design is presented and applied to infer the marine atmospheric duct using the refractivity from clutter technique, and the radar sea clutter power is simulated by the commonly used parabolic equation method. In order to test the accuracy of the OCABC algorithm, the measured data and the simulated clutter power with different noise levels are, respectively, utilized to estimate the evaporation duct and surface duct. The estimation results obtained by the proposed algorithm are also compared with those of the comprehensive learning particle swarm optimizer and the artificial bee colony algorithm combined with opposition-based learning and global best search equation. The comparison results demonstrate that the performance of proposed algorithm is better than those of the compared algorithms for the marine atmospheric duct estimation.

Keywords

Artificial bee colonyatmospheric duct estimationorthogonal crossoverorthogonal experimental designradar sea clutter
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 10 , Issue 4 , May 2018 , pp. 437 - 445
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Yardim, C, Gerstoft, P and Hodgkiss, WS (2008) Tracking refractivity from clutter using Kalman and particle filters. IEEE Transactions on Antennas and Propagation 56, 1058–1070.Google Scholar
[2]Yardim, C, Gerstoft, P and Hodgkiss, WS (2009) Sensitivity analysis and performance estimation of refractivity from clutter techniques. Radio Science 44, RS1008.Google Scholar
[3]Rogers, LT, Hattan, CP and Stapleton, JK (2000) Estimating evaporation duct heights from radar sea echo. Radio Science 35, 955–966.Google Scholar
[4]Gerstoft, P, Rogers, LT, Krolik, LL and Hodgkiss, WS (2003) Inversion for refractivity parameters from radar sea clutter. Radio Science 38, 8053.Google Scholar
[5]Karimian, A, Yardim, C, Gerstoft, P, Hodgkiss, WS and Barrios, AE (2011) Refractivity estimation from sea clutter: an invited review. Radio Science 46, RS6013.Google Scholar
[6]Yardim, C, Gerstoft, P and Hodgkiss, WS (2006) Estimation of radio refractivity from radar clutter using Bayesian Monte Carlo analysis. IEEE Transactions on Antennas and Propagation 54, 1318–1327.Google Scholar
[7]Vasudevan, S, Anderson, RH, Kraut, S, Gerstoft, P, Rogers, LT and Krolik, JL (2007) Recursive Bayesian electromagnetic refractivity estimation from radar sea clutter. Radio Science 42, RS2014.Google Scholar
[8]Douvenot, R, Fabbro, V, Gerstoft, P, Bourlier, C and Saillard, J (2008) A duct mapping method using least squares support vector machines. Radio Science 43, RS6005.Google Scholar
[9]Wang, B, Wu, ZS, Zhao, Z and Wang, HG (2009) Retrieving evaporation duct heights from radar sea clutter using particle swarm optimization (PSO) algorithm. Progress in Electromagnetic Research M 9, 79–91.CrossRefGoogle Scholar
[10]Zhao, XF, Huang, SX, Xiang, J and Shi, WL (2011) Remote sensing of atmospheric duct parameters using simulated annealing. Chinese Physics B 20, 099201.Google Scholar
[11]Zhang, JP, Wu, ZS, Zhu, QL and Wang, B (2011) A four-parameter M-profile model for the evaporation duct estimation from radar clutter. Progress in Electromagnetics Research 114, 353–368.Google Scholar
[12]Karaboga, D and Basturk, B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39, 459–471.Google Scholar
[13]Karaboga, D, Gorkemli, B, Ozturk, C and Karaboga, N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review 42, 21–57.Google Scholar
[14]Yang, C (2013) Estimation of the atmospheric duct from radar sea clutter using artificial bee colony optimization algorithm. Progress in Electromagnetics Research 135, 183–199.CrossRefGoogle Scholar
[15]Yang, C, Zhang, JK and Guo, LX (2016) Investigation on the inversion of the atmospheric duct using the artificial bee colony algorithm based on opposition-based learning. International Journal of Antennas and Propagation 2016, 2749035.Google Scholar
[16]Zhu, GP and Kwong, S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computation 217, 3166–3173.Google Scholar
[17]Barrios, AE (1994) A terrain parabolic equation model for propagation in the troposphere. IEEE Transactions on Antennas and Propagation 42, 90–98.Google Scholar
[18]Sirkova, I (2012) Brief review on PE method application to propagation channel modeling in sea environment. Central European Journal of Engineering 2, 19–38.Google Scholar
[19]Gao, WF, Liu, SY and Huang, LL (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Transactions in Cybernetics 43, 1011–1024.Google ScholarPubMed
[20]Leung, YW and Wang, Y (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation 5, 41–53.Google Scholar
[21]Zhan, ZH, Zhang, J, Li, Y and Shi, YH (2011) Orthogonal learning particle swarm optimization. IEEE Transactions on Evolutionary Computation 15, 832–847.Google Scholar
[22]Wang, Y, Cai, Z and Zhang, Q (2012) Enhancing the search ability of differential evolution through orthogonal crossover. Information Sciences 185, 153–177.Google Scholar
[23]Xiong, G, Shi, D and Duan, X (2014) Enhancing the performance of biogeography-based optimization using polyphyletic migration operator and orthogonal learning. Computers & Operations Research 41, 125–139.CrossRefGoogle Scholar
[24]Liang, JJ, Qin, AK, Suganthan, PN and Baskar, S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10, 281–295.Google Scholar
[25]Notarnicola, C, Angiulli, M and Posa, F (2008) Soil moisture retrieval from remotely sensed data: neural network approach versus Bayesian method. IEEE Transactions on Geoscience and Remote Sensing 46, 547–557.Google Scholar