基于空间分布熵的随机辐射源布局优化
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摘要
针对微波凝视关联成像中,随机辐射源布局优化以随机辐射场矩阵的有效秩最大化为准则时面临目标函数计算复杂、效率太低的问题,提出了一种基于空间分布熵的布局优化方法。首先,构建了一种以空间分布熵来定量表征随机辐射源布局随机性的方法,并通过仿真分析验证了随机辐射源的空间分布熵与随机辐射场矩阵的有效秩之间的总体正相关性;然后,采用遗传算法以空间分布熵最大化为准则对随机辐射源布局进行了优化;最后,通过成像仿真验证了随机辐射源布局优化能有效提高微波凝视关联成像性能。
In microwave staring correlated imaging, the distribution optimization of the stochastic radiation source is now based on the maximum effective rank of the stochastic radiation field matrix. However, the efficiency of the optimization algorithm is too low because of the complex computation of the objective function. Focusing on this problem, a distribution optimization method based on spatial distribution entropy is proposed in this paper. Firstly, the concept of spatial distribution entropy is established to quantitatively characterize the randomness of the stochastic radiation source distribution, and the positive correlation between the spatial distribution entropy and the effective rank of the stochastic radiation field matrix is verified. Then, using the spatial distribution entropy as the objective function, genetic algorithm is introduced to optimize the distribution of the stochastic radiation source. Finally, the simulation results illustrate distribution optimization of the stochastic radiation source effectively improves the MSCI performance.
In microwave staring correlated imaging, the distribution optimization of the stochastic radiation source is now based on the maximum effective rank of the stochastic radiation field matrix. However, the efficiency of the optimization algorithm is too low because of the complex computation of the objective function. Focusing on this problem, a distribution optimization method based on spatial distribution entropy is proposed in this paper. Firstly, the concept of spatial distribution entropy is established to quantitatively characterize the randomness of the stochastic radiation source distribution, and the positive correlation between the spatial distribution entropy and the effective rank of the stochastic radiation field matrix is verified. Then, using the spatial distribution entropy as the objective function, genetic algorithm is introduced to optimize the distribution of the stochastic radiation source. Finally, the simulation results illustrate distribution optimization of the stochastic radiation source effectively improves the MSCI performance.
引文
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[15] 李蕾, 王建明, 伍光新. 基于自适应遗传算法的稀布阵天线优化[J]. 现代雷达, 2017, 39(3): 59-61.LI Lei, WANG Jianming, WU Guangxin. Optimization of sparse array based on adaptive genetic algorithm[J]. Modern Radar, 2017, 39(3): 59-61.
[16] 罗源伟, 李泉永, 李文勇, 等. 基于遗传算法的天线结构优化[J]. 现代雷达, 2000, 22(4): 67-71.LUO Yuanwei,LI Quanyong, LI Wenyong, et al. Structural optimization of antenna via genetic algorithms[J]. Modern Radar, 2000, 22(4): 67-71.
[2] GUO Y Y, WANG D J, HE X Z, et al. Super-resolution imaging method based on random radiation radar array[C]// 2012 IEEE International Conference on Imaging Systems and Techniques (IST). Manchester, UK: IEEE Press, 2012: 1-6.
[3] HE X Z, LIU B, CHAI S G, et al. A novel approach of high spatial-resolution microwave staring imaging[C]// 2013 Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). Tsukuba, Japan: IEEE Press, 2013: 75-78.
[4] MA Y, HE X, MENG Q, et al. Microwave staring correlated imaging and resolution analysis[M]. Geo-Informatics in Resource Management and Sustainable Ecosystem. Berlin: Springer Berlin Heidelberg, 2013: 737-747.
[5] ZHA G. Effect analysis and design on array geometry for coincidence imaging radar based on effective rank theory[C]// LSPRS International Conference on Computer Vision in Remote Sensing. Xiamen, China: SPIE Press, 2016: 1-8.
[6] YANG H T, ZHANG L J, GAO Y S, et al. Azimuth wavefront modulation using plasma lens array for microwave staring imaging[C]// 2015 IEEE International Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS) Milan, Italy: IEEE Press, 2015: 4276-4379.
[7] HUNT J, GOLLUB J, DRISCOLL T, et al. Metamaterial microwave holographic imaging system[J]. Journal of the Optical Society of America, 2014, 31(10): 2109-2119.
[8] WATTS C M, SHREKENHAMER D, MONTOYA J, et al. Terahertz compressive imaging with metamaterial spatial light modulators[J]. Nature Photonics, 2014, 8(8): 605-609.
[9] LI D, LI X, QIN Y, et al. Radar coincidence imaging: an instantaneous imaging technique with stochastic signals[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4): 2261-2277.
[10] CHENG Y, ZHOU X, XU X, et al. Radar coincidence imaging with stochastic frequency modulated array[J]. IEEE Journal of Selected Topics in Signal Processing, 2016, 11(2): 414-417.
[11] CHA Jianzhong, TANG Xiaojun, LU Yiping. Survey on packing problems[J]. Journal of Computer Aided Design & Computer Graphics,2002, 14(8): 705-712.
[12] GUO Y Y, HE X Z, WANG D J. A novel super-resolution imaging method based on stochastic radiation radar array[J]. Measurement Science and Technology, 2013, 24(7): 074013.
[13] 姜丹. 信息论与编码[M]. 合肥:中国科学技术大学出版社, 2004.JIANG Dan. Information theory and coding[M]. Hefei: University of Science and Technology of China Press, 2004.
[14] SRINIVAS M,PATNAIK L M.Adaptive probabilities of crossover and mutation in genetic algorithms[J].IEEE Transactions on Systems,Man and Cvbemctics, 1994,24(4):656-667.
[15] 李蕾, 王建明, 伍光新. 基于自适应遗传算法的稀布阵天线优化[J]. 现代雷达, 2017, 39(3): 59-61.LI Lei, WANG Jianming, WU Guangxin. Optimization of sparse array based on adaptive genetic algorithm[J]. Modern Radar, 2017, 39(3): 59-61.
[16] 罗源伟, 李泉永, 李文勇, 等. 基于遗传算法的天线结构优化[J]. 现代雷达, 2000, 22(4): 67-71.LUO Yuanwei,LI Quanyong, LI Wenyong, et al. Structural optimization of antenna via genetic algorithms[J]. Modern Radar, 2000, 22(4): 67-71.