Estimation of surface-based duct parameters from automatic identification system using the Levy flight quantum-behaved particle swarm optimization algorithm

References

• Sirkova I, Mikhalev M. Parabolic-equation-based study of ducting effects on microwave propagation. J Microw Opt Technol Letters. 2004;42(5):390–394. doi: 10.1002/mop.20314
   Web of Science ®Google Scholar
• Yardim C, Gerstoft P, Hodgkiss WS. Sensitivity analysis and performance estimation of refractivity from clutter techniques. Radio Sci. 2009;44(RS1008):1–16.
   Google Scholar
• Zhang JP, Wu ZS, Hu RX. Combined estimation of low-altitude radio refractivity based on sea clutters from multiple shipboard radars. J Electromagnet Wave. 2011;25(8–9):1201–1212. doi: 10.1163/156939311795762033
   Web of Science ®Google Scholar
• Zhang ZH, Sheng Z, Shi HQ. Parameter estimation of atmospheric refractivity from radar clutter using the particle swarm optimization via lévy flight. J Appl Remote Sens. 2015;9:095998. doi: 10.1117/1.JRS.9.095998
   Web of Science ®Google Scholar
• Karimian A, Yardim C, Gerstoft P, et al. Refractivity estimation from sea clutter: An invited review. Radio Sci. 2011;46(RS6013):1–16.
   Google Scholar
• Creech JA, Ryan JF. AIS: The cornerstone of national security? J Navigation. 2003;56:31–44. doi: 10.1017/S0373463302002072
   Web of Science ®Google Scholar
• Safety of life at sea (SOLAS). Convention, Chapter V, regulation 19; 2000.
   Google Scholar
• Tech. Rep. ITU–R M.2123. Long range detection of automatic identification system (AIS) messages under various tropospheric propagation conditions. International Telecommunications Union (ITU); 2007.
   Google Scholar
• Green D, Fowler C, Power D, et al. VHF propagation study. London: London Research and Development Corporation; 2011; (Contractor report; no. DRDC Atlantic CR 2011–2152).
   Google Scholar
• Bruin ER. On propagation effects in maritime situation awareness: Modelling the impact of North Sea weather conditions on the performance of AIS and coastal radar systems [master’s thesis]. Utrecht: Utrecht University; 2016.
   Google Scholar
• Kennedy J, Eberhart R. Particle swarm optimization. IEEE international Conference on Neural networks. 1995 Nov 27–Dec 1; Perth (WA): IEEE Press; 1995. p. 1942–1948.
   Google Scholar
• Zhang L, Yang F, Elsherbeni AZ. On the use of random variables in particle swarm optimizations: A comparative study of Gaussian and uniform distributions. J Electromagnet Wave. 2009;23(5–6):711–721. doi: 10.1163/156939309788019787
   Web of Science ®Google Scholar
• Sun J, Feng B, Xu WB. Particle swarm optimization with particles having quantum behavior. IEEE Congress on evolutionary Computation. 2004 Jun 19–23; Portland (OR): IEEE Press; 2004. p. 325–331.
   Google Scholar
• Sun J, Wu XJ, Palade V, et al. Convergence analysis and improvements of quantum-behaved particle swarm optimization. Inform Sciences. 2012;193(15):81–103. doi: 10.1016/j.ins.2012.01.005
   Google Scholar
• Liu TY, Jiao LC, Ma WP, et al. Quantum-behaved particle swarm optimization with collaborative attractors for nonlinear numerical problems. Commun Nonlinear Sci Numer Simulat. 2017;44:167–183. doi: 10.1016/j.cnsns.2016.08.001
   Web of Science ®Google Scholar
• Rehman OU, Yang JQ, Zhou Q, et al. A modified QPSO algorithm applied to engineering inverse problems in electromagnetic. Int J Appl Electrom. 2017;54:107–121.
   Web of Science ®Google Scholar
• Yang XS. Firefly algorithm, lévy flights and global optimization. In: Research and Development in intelligent systems XXVI. London: Springer; 2010. p. 209–218.
   Google Scholar
• Yang C, Guo LX. Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm. Int J Microw Wirel T. 2018;10(4):437–445. doi: 10.1017/S1759078718000247
   Web of Science ®Google Scholar
• Sirkova I. Brief review on PE method application to propagation channel modeling in sea environment. Central Euro J Eng. 2012;2(1):19–38.
   Google Scholar
• Sirkova I. Propagation factor and path loss simulation results for two rough surface reflection coefficients applied to the microwave ducting propagation over the sea. Prog Electromagn Res M. 2011;17:151–166. doi: 10.2528/PIERM11020602
   Google Scholar
• Clerc M, Kennedy J. The particle swarm: Explosion, stability and convergence in a multidimentional complex space. IEEE T Evolut Comput. 2002;6(1):58–73. doi: 10.1109/4235.985692
   Web of Science ®Google Scholar
• Sun J, Xu WB, Feng B. A global search strategy of quantum-behaved particle swarm optimization. IEEE Conference on Cybernetics and intelligent systems; 2004 Dec 1–3; Singapore: IEEE Press; 2004. p. 111–116.
   Google Scholar
• Jensi R, Jiji GW. An enhanced particle swarm optimization with Levy flight for global optimization. Appl Soft Comput. 2016;43:248–261. doi: 10.1016/j.asoc.2016.02.018
   Web of Science ®Google Scholar
• Mantegna RN. Fast, accurate algorithm for numerical simulation of lévy stable stochastic processes. Phys Rev E. 1994;49:451–485. doi: 10.1103/PhysRevE.49.4677
   Web of Science ®Google Scholar
• Notarnicola C, Angiulli M, Posa F. Soil moisture retrieval from remotely sensed data: Neural network approach versus Bayesian method. IEEE T Geosci Remote. 2008;46(2):547–557. doi: 10.1109/TGRS.2007.909951
   Web of Science ®Google Scholar