分布式雷达节点位置优化的多约束遗传算法研究
|
摘要
分布式雷达是近年来国内外广泛关注的一种新体制雷达,具有机动性强、成本低、可靠性高等优点。但是由于分布式雷达节点的稀疏布置,容易产生栅瓣、高旁瓣等问题,严重影响雷达系统性能。本文基于多约束遗传算法提出了一种分布式雷达节点位置优化方法。该方法首先基于约束最小间距对节点位置进行实值映射编码,并对遗传算法的种群进行初始化,然后对种群进行先增广后收缩的选择处理,再根据自适应概率进行交叉和变异处理,最后经迭代实现分布式雷达节点位置的优化设计。该方法不仅能够有效抑制系统栅瓣,还能够满足多约束需求显著降低系统旁瓣电平。与已有方法相比,该方法简单易行、全局搜索能力强。通过仿真验证了所提方法的有效性和稳健性。
Distributed radar was a new type of radar that raised wide concern at home and abroad in recent years. It had good maneuverability, low cost and high reliability. However, due to the sparse arrangement of distributed radar elements, grating lobes and high side lobes problems were easy to occur. It badly influenced the performance of the array. In order to optimize the beam pattern of the distributed array radar, a method based on multi-constrained genetic algorithm was proposed. In the proposed method, firstly real valued mapping coding of chromosome with minimum element spacing constraint was used and the population was initialed. Then a modified election operator which augments and then abates the population was applied to the individuals of the next iteration. Next, the broad sense crossover and mutation operator with adaptive parameters were utilized. The method can not only effectively suppress the system grating lobes, but also significantly reduce the system side-lobe level with multiple constraints. Compared with the existing methods, the method proposed is simple and easy, and has stronger global search ability. The simulation results demonstrate the effectiveness and robustness of the proposed algorithm.
Distributed radar was a new type of radar that raised wide concern at home and abroad in recent years. It had good maneuverability, low cost and high reliability. However, due to the sparse arrangement of distributed radar elements, grating lobes and high side lobes problems were easy to occur. It badly influenced the performance of the array. In order to optimize the beam pattern of the distributed array radar, a method based on multi-constrained genetic algorithm was proposed. In the proposed method, firstly real valued mapping coding of chromosome with minimum element spacing constraint was used and the population was initialed. Then a modified election operator which augments and then abates the population was applied to the individuals of the next iteration. Next, the broad sense crossover and mutation operator with adaptive parameters were utilized. The method can not only effectively suppress the system grating lobes, but also significantly reduce the system side-lobe level with multiple constraints. Compared with the existing methods, the method proposed is simple and easy, and has stronger global search ability. The simulation results demonstrate the effectiveness and robustness of the proposed algorithm.
引文
[1] 殷丕磊.地基宽带分布式全相参雷达技术研究[D].北京:北京理工大学,2016.Yin Pilei.Research on Ground-Based Wideband Distributed Coherent Aperture Radar[D].Beijing:Beijing Institute of Technology,2016.(in Chinese)
[2] Yang Xiaopeng,Yin Pilei,Zeng Tao,et al.Applying Auxiliary Array to Suppress Mainlobe Interference for Ground-Based Radar[J].IEEE Antennas and Wireless Propagation Letters,2013,12:433- 436.
[3] Feng Han Zhe,Liu Hong Wei,Yan Jun Kun,et al.A fast efficient power allocation algorithm for target localization in cognitive distributed multiple radar systems☆[J].Signal Processing,2016,127:100-116.
[4] 张帅.分布式雷达抗干扰与目标检测方法研究[D].成都:电子科技大学,2017.Zhang Shuai.Research of Distributed Radar Technology Against Jamming And Target Detection[D].Chengdu:University of Electronic Science and Technology of China,2017.(in Chinese)
[5] Yan Chuang,Yang Peng,Xing Zhiyu,et al.Synthesis of Planar Sparse Arrays With Minimum Spacing Constraint[J].IEEE Antennas & Wireless Propagation Letters,2018,PP(99):1-1.
[6] Keizer,Will P M N.Linear Array Thinning Using Iterative FFT Techniques[J].IEEE Transactions on Antennas and Propagation,2008,56(8):2757-2760.
[7] Liu Yanhui,Nie Zaiping,Liu Qinghuo.Reducing the Number of Elements in a Linear Antenna Array by the Matrix Pencil Method[J].IEEE Transactions on Antennas and Propagation,2008,56(9):2955-2962.
[8] Pinchera D,Migliore M D,Panariello G.Synthesis of Large Sparse Arrays Using IDEA (Inflating-Deflating Exploration Algorithm)[J].IEEE Transactions on Antennas and Propagation,2018:1-1.
[9] Haupt,R.L.Thinned arrays using genetic algorithms[J].IEEE Transactions on Antennas and Propagation,1994,42(7):993-999.
[10] Jin N,Rahmat-Samii Y.Advances in Particle Swarm Optimization for Antenna Designs:Real-Number,Binary,Single-Objective and Multiobjective Implementations[J].IEEE Transactions on Antennas and Propagation,2007,55(3):556-567.
[11] Chen Kesong,He Zihui,Han C.A Modified Real GA for the Sparse Linear Array Synthesis With Multiple Constraints[J].IEEE Transactions on Antennas and Propagation,2006,54(7):2169-2173.
[12] Cui Can,Li Wentao,Ye Xiutiao,et al.Hybrid Genetic Algorithm and Modified Iterative Fourier Transform Algorithm for Large Thinned Array Synthesis[J].IEEE Antennas & Wireless Propagation Letters,2017,PP(99):1-1.
[13] Fu Yu,Guo Zhigui,Wang Haowen,et al.Optimization of planar thinned antenna array based on genetic and convex hybrid algorithm[C]//Progress in Electromagnetic Research Symposium.IEEE,2016.
[14] Srinivas M,Patnaik L M.Adaptive probabilities of crossover and mutation in genetic algorithms[J].IEEE Transactions on Systems,Man and Cybernetics,1994,24(4):656- 667.
[2] Yang Xiaopeng,Yin Pilei,Zeng Tao,et al.Applying Auxiliary Array to Suppress Mainlobe Interference for Ground-Based Radar[J].IEEE Antennas and Wireless Propagation Letters,2013,12:433- 436.
[3] Feng Han Zhe,Liu Hong Wei,Yan Jun Kun,et al.A fast efficient power allocation algorithm for target localization in cognitive distributed multiple radar systems☆[J].Signal Processing,2016,127:100-116.
[4] 张帅.分布式雷达抗干扰与目标检测方法研究[D].成都:电子科技大学,2017.Zhang Shuai.Research of Distributed Radar Technology Against Jamming And Target Detection[D].Chengdu:University of Electronic Science and Technology of China,2017.(in Chinese)
[5] Yan Chuang,Yang Peng,Xing Zhiyu,et al.Synthesis of Planar Sparse Arrays With Minimum Spacing Constraint[J].IEEE Antennas & Wireless Propagation Letters,2018,PP(99):1-1.
[6] Keizer,Will P M N.Linear Array Thinning Using Iterative FFT Techniques[J].IEEE Transactions on Antennas and Propagation,2008,56(8):2757-2760.
[7] Liu Yanhui,Nie Zaiping,Liu Qinghuo.Reducing the Number of Elements in a Linear Antenna Array by the Matrix Pencil Method[J].IEEE Transactions on Antennas and Propagation,2008,56(9):2955-2962.
[8] Pinchera D,Migliore M D,Panariello G.Synthesis of Large Sparse Arrays Using IDEA (Inflating-Deflating Exploration Algorithm)[J].IEEE Transactions on Antennas and Propagation,2018:1-1.
[9] Haupt,R.L.Thinned arrays using genetic algorithms[J].IEEE Transactions on Antennas and Propagation,1994,42(7):993-999.
[10] Jin N,Rahmat-Samii Y.Advances in Particle Swarm Optimization for Antenna Designs:Real-Number,Binary,Single-Objective and Multiobjective Implementations[J].IEEE Transactions on Antennas and Propagation,2007,55(3):556-567.
[11] Chen Kesong,He Zihui,Han C.A Modified Real GA for the Sparse Linear Array Synthesis With Multiple Constraints[J].IEEE Transactions on Antennas and Propagation,2006,54(7):2169-2173.
[12] Cui Can,Li Wentao,Ye Xiutiao,et al.Hybrid Genetic Algorithm and Modified Iterative Fourier Transform Algorithm for Large Thinned Array Synthesis[J].IEEE Antennas & Wireless Propagation Letters,2017,PP(99):1-1.
[13] Fu Yu,Guo Zhigui,Wang Haowen,et al.Optimization of planar thinned antenna array based on genetic and convex hybrid algorithm[C]//Progress in Electromagnetic Research Symposium.IEEE,2016.
[14] Srinivas M,Patnaik L M.Adaptive probabilities of crossover and mutation in genetic algorithms[J].IEEE Transactions on Systems,Man and Cybernetics,1994,24(4):656- 667.