A New Optimizing Method for Planar Arrays Pattern
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- 英文论文题名：A New Optimizing Method for Planar Arrays Pattern
- 论文作者：Yongbin Chou ; Xianlin Huang ; Shuchun Zhang ; Wei Jiang
- 英文论文作者：Yongbin Chou ; Xianlin Huang ; Shuchun Zhang ; Wei Jiang ; Center for Control Theory and Guidance Technology ; Harbin Institute of Technology ; Flight Simulation Technology Research Institute ; Air Force Harbin Flight Academy
- 年：2017
- 作者机构：Center for Control Theory and Guidance Technology,Harbin Institute of Technology;Flight Simulation Technology Research Institute,Air Force Harbin Flight Academy;
- 英文论文关键词：planar array ; sidelobes interference ; pattern optimization ; Particle Swarm Optimization(PSO)
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TP18
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：370k
- 原文格式：D
- 会议级别：全国
摘要
Traditional particle swarm optimization(PSO) will be failed because of falling into local optimum solutions and converging too slowly when being used to optimize planar array pattern. So a new method is presented to improve the traditional PSO convergence by means of efficient estimation of the optimum particle's initial values. A desired pattern is first constructed, and then the corresponding aperture weights can be estimated by means of matrix operation. These weights then are assigned to a particle as initial values(this operation is equivalent to making an efficient estimation of the optimum particle's initial values), while the other particles are initialized randomly, then the traditional PSO algorithm is used to search for the global best solutions. The simulation results proved that this method could rapidly converge to satisfying global solutions and the desired aperture weights could be achieved. So this improved PSO method presented here is far better than the traditional PSO to solve the optimization problems of planar array pattern.
Traditional particle swarm optimization(PSO) will be failed because of falling into local optimum solutions and converging too slowly when being used to optimize planar array pattern. So a new method is presented to improve the traditional PSO convergence by means of efficient estimation of the optimum particle's initial values. A desired pattern is first constructed, and then the corresponding aperture weights can be estimated by means of matrix operation. These weights then are assigned to a particle as initial values(this operation is equivalent to making an efficient estimation of the optimum particle's initial values), while the other particles are initialized randomly, then the traditional PSO algorithm is used to search for the global best solutions. The simulation results proved that this method could rapidly converge to satisfying global solutions and the desired aperture weights could be achieved. So this improved PSO method presented here is far better than the traditional PSO to solve the optimization problems of planar array pattern.
Traditional particle swarm optimization(PSO) will be failed because of falling into local optimum solutions and converging too slowly when being used to optimize planar array pattern. So a new method is presented to improve the traditional PSO convergence by means of efficient estimation of the optimum particle's initial values. A desired pattern is first constructed, and then the corresponding aperture weights can be estimated by means of matrix operation. These weights then are assigned to a particle as initial values(this operation is equivalent to making an efficient estimation of the optimum particle's initial values), while the other particles are initialized randomly, then the traditional PSO algorithm is used to search for the global best solutions. The simulation results proved that this method could rapidly converge to satisfying global solutions and the desired aperture weights could be achieved. So this improved PSO method presented here is far better than the traditional PSO to solve the optimization problems of planar array pattern.
引文
[1]Active Electronically Scanned Array(AESA)Fire Control Radars.http://www.northropgrumman.com.
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[8]R.Bera,D.Mandal,S.P.Ghoshal.Optimal design of concentric elliptical array antenna for maximum side-lobe level reduction using particle swarm optimization with aging leader and challengers.International Conference on Communication and Signal Processing(ICCSP),2016:1817-1821.
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[2]S.Moore.UK Airborne AESA Radar Research.IEEE Aerospace and Electronic Systems Magazine,25(2):29-35,2010.
[3]Haupt R.Antenna Arrays:A Computational Approach.JOHN WILEY&SONS,INC.,115-283,2010.
[4]B.R.Mahafza,Z.Elsherbeni.MATLAB Simulations For Radar Systems Design.Chapman&Hall/CRC LLC,2004,8.5.
[5]X.Yu,Y.Zhang,T.Dong.Design of Low Side-lobe Level Conformal Antenna Array by PSO-GA.IEEE International Conference on Ubiquitous Wireless Broadband(ICUWB),2016:1-3.
[6]A.L.Hadj,B.Boussouar.New Optimisation Algorithm for Planar Antenna Array Synthesis.AEU-International Journal of Electronics and Communications,66(9):752-757,2012.
[7]R.Ghatak,A.Karmakar,D.R.Poddar.Evolutionary Optimization of Haferman Carpet Fractal Patterned Antenna Array.International Journal of RF and Microwave Computer-Aided Engineering,1-11,2015.
[8]R.Bera,D.Mandal,S.P.Ghoshal.Optimal design of concentric elliptical array antenna for maximum side-lobe level reduction using particle swarm optimization with aging leader and challengers.International Conference on Communication and Signal Processing(ICCSP),2016:1817-1821.
[9]L.Manica,M.Carlin,I.Malcic.Wideband Multilayer WAIM Design and Optimization.8th European Conference on Antennas and Propagation,2014:2997-3000.
[10]G.G.Lema,G.T.Tesfamariam,M.I.Mohammed.A Novel Elliptical-Cylindrical Antenna Array for Radar Applications.IEEE Transactions on Antennas and Propagation.64(5):1681-1688,2016.
[11]A.D.Brown.Electronically Scanned Arrays-MATLAB Modeling and Simulation.CRC Press,Taylor&Francis Group,LLC,2012:35-80.
[12]A.Rezagholi,F.Mohajeri.Directivity optimization of fractal antenna arrays using PSO algorithm.IEEE 24th Iranian Conference on Electrical Engineering(ICEE),2016:1224–1228.
[13]J.Kennedy,R.C.Eberhart.Particle swarm optimization.Poc.IEEE int’l conf.on neural networks IEEE service center,Piscataway,NJ,1995.Vol.IV:1942-1948.
[14]Y.Shi,R.C.Eberhart.A modified particle swarm optimizer.Proceedings of the IEEE International Conference on Evolutionary Computation.IEEE Press,Piscataway,NJ,1998:69-73.