大地等效电导率反演研究
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摘要
罗兰C是一种广泛用于航海、航空、公路交通等领域的陆基中远程无线电导航系统。然而,低频地波传播时延误差限制着罗兰C系统的定位精度,对传播时延进行有效修正可提高罗兰C定位精度。低频地波传播时延修正的理论预测是传播时延修正的主要方法,而大地等效电导率的精度又是影响修正量理论预测精度的主要因素。因此获取罗兰C覆盖区的大地等效电导率分布,对提高罗兰C传播时延修正具有重要意义。
本文研究了利用传播时延修正实测数据反演大地等效电导率的方法。以低频地波传播时延修正理论预测算法为前向算法,把反演问题转换为优化问题,在对遗传算法和免疫算法分析研究的基础上,提出了结合混沌和遗传的混沌免疫遗传克隆选择算法(CIGCSA)。具体内容如下：
本文首先介绍了低频地波传播时延计算的理论方法,重点对基于均匀光滑路径的留数级数法和基于分段不均匀路径的Millington经验公式法进行编程实现与仿真分析。
其次,对智能优化方法中的遗传算法和免疫算法的理论基础和实现方法进行了研究,通过对测试函数的优化分析了两种算法的优劣。而后将两种算法结合并对算法进行改进,提出了混沌免疫遗传克隆选择算法(CIGCSA):该算法引入排序和交叉算子来提高算法的局部搜索能力；同时还引入混沌算子来增加种群的多样性,提高算法全局搜索能力和收敛速度。
最后,将CIGCSA算法应用于大地电导率的反演中,从仿真实验可以看出,基于CIGCSA的分段不均匀光滑路径反演出的电导率稳定性好,可靠性高,误差小。
本文的研究结果可为我国大地等效电导率的反演提供一定参考。
Loran-C is a long-range land-based radio navigation system which is widely used in aviation, maritime, road transport and other areas. However, the positioning accuracy of Loran-C system is limited by the time-delay error of the low-frequency ground-wave propagation and can be increased by correcting propagation time-delay. The theoretic forecast is the main way to the correction of propagation time-delay, and the earth equivalent conductivity is the key factor which influences the theoretic prediction accuracy of the propagation time-delay correction. Therefore, obtaining the distribution of earth equivalent conductivity in the Loran-C coverage areas is very significant to improve the correction of Loran-C propagation time-delay.
In this paper, the method of inversing the earth equivalent conductivity by the measured data of propagation time-delay is researched. Make the theoretic forecast algorithm of the correction of low-frequency ground-wave time-delay as the forward algorithm, and then the inversion problem can be converted to an optimization problem. Based on the research of the Genetic Algorithm and Immune Algorithm, the Chaos Immune Genetic Clonal Selection Algorithm (CIGCSA) which combined chaos, genetic and clonal selection is presented. The concrete content is as follows:
This paper first introduced the theoretical approach of the low-frequency ground-wave propagation time-delay calculation, focusing on the programming and simulation analysis about the residue-series method based on the uniform smooth path and the Millington empirical formula based on the piecewise non-uniform smooth path.
Secondly, this paper researched the theoretical basis and implementation method about genetic algorithm and the immune algorithm of intelligent optimization, and analyzes the pros and cons of the two algorithms by optimizing the test function, and then presented the improvement algorithm called the Chaos Immune Genetic Clones Algorithm (CIGCSA). This algorithm introduces crossover-operator to increase the capacity of local search; and chaos operator to increase the diversity of population, improve the capacity of global search and accelerate the speed of convergence.
Finally, the algorithm CIGCSA is applied to the earth equivalent conductivity inversion. From the simulation results, we can see that the inversed conductivity has the better stability, higher reliability and smaller error.
The researching result in this paper can provide some references for inversion of the earth equivalent conductivity in our country.
本文研究了利用传播时延修正实测数据反演大地等效电导率的方法。以低频地波传播时延修正理论预测算法为前向算法,把反演问题转换为优化问题,在对遗传算法和免疫算法分析研究的基础上,提出了结合混沌和遗传的混沌免疫遗传克隆选择算法(CIGCSA)。具体内容如下：
本文首先介绍了低频地波传播时延计算的理论方法,重点对基于均匀光滑路径的留数级数法和基于分段不均匀路径的Millington经验公式法进行编程实现与仿真分析。
其次,对智能优化方法中的遗传算法和免疫算法的理论基础和实现方法进行了研究,通过对测试函数的优化分析了两种算法的优劣。而后将两种算法结合并对算法进行改进,提出了混沌免疫遗传克隆选择算法(CIGCSA):该算法引入排序和交叉算子来提高算法的局部搜索能力；同时还引入混沌算子来增加种群的多样性,提高算法全局搜索能力和收敛速度。
最后,将CIGCSA算法应用于大地电导率的反演中,从仿真实验可以看出,基于CIGCSA的分段不均匀光滑路径反演出的电导率稳定性好,可靠性高,误差小。
本文的研究结果可为我国大地等效电导率的反演提供一定参考。
Loran-C is a long-range land-based radio navigation system which is widely used in aviation, maritime, road transport and other areas. However, the positioning accuracy of Loran-C system is limited by the time-delay error of the low-frequency ground-wave propagation and can be increased by correcting propagation time-delay. The theoretic forecast is the main way to the correction of propagation time-delay, and the earth equivalent conductivity is the key factor which influences the theoretic prediction accuracy of the propagation time-delay correction. Therefore, obtaining the distribution of earth equivalent conductivity in the Loran-C coverage areas is very significant to improve the correction of Loran-C propagation time-delay.
In this paper, the method of inversing the earth equivalent conductivity by the measured data of propagation time-delay is researched. Make the theoretic forecast algorithm of the correction of low-frequency ground-wave time-delay as the forward algorithm, and then the inversion problem can be converted to an optimization problem. Based on the research of the Genetic Algorithm and Immune Algorithm, the Chaos Immune Genetic Clonal Selection Algorithm (CIGCSA) which combined chaos, genetic and clonal selection is presented. The concrete content is as follows:
This paper first introduced the theoretical approach of the low-frequency ground-wave propagation time-delay calculation, focusing on the programming and simulation analysis about the residue-series method based on the uniform smooth path and the Millington empirical formula based on the piecewise non-uniform smooth path.
Secondly, this paper researched the theoretical basis and implementation method about genetic algorithm and the immune algorithm of intelligent optimization, and analyzes the pros and cons of the two algorithms by optimizing the test function, and then presented the improvement algorithm called the Chaos Immune Genetic Clones Algorithm (CIGCSA). This algorithm introduces crossover-operator to increase the capacity of local search; and chaos operator to increase the diversity of population, improve the capacity of global search and accelerate the speed of convergence.
Finally, the algorithm CIGCSA is applied to the earth equivalent conductivity inversion. From the simulation results, we can see that the inversed conductivity has the better stability, higher reliability and smaller error.
The researching result in this paper can provide some references for inversion of the earth equivalent conductivity in our country.
引文
[1]Pierce J. A. An introduction to Loran[J]. Aerospace and Electronic Systems Magazine, IEEE,1990, 5(10):16-33.
[2]吴海涛.罗兰_C最新进展及其对我国相关系统改造的启示[J].陕西天文台台刊,2000,23(1)：10-18.
[3]陈秀明.罗兰C组合导航中地波ASF修正的研究[D].陕西.西安：2007：6-13,27-35.
[4]Basker S., Williams P., Bransby M., et al. Enhanced Loran:Real-time maritime trials[C]. Position, Location and Navigation Symposium,2008 IEEE/ION.2008:792.
[5]袁海波,陈洪卿,王宏远.北斗长河组合导航伪时差测量与ASF修正[J].宇航计测技术,2004,(03)：5-7.
[6]Johnson G. W., Swaszek P. F., Hartnett R. J., et al. An Evaluation of eLoran as a Backup to GPS[C]. IEEE Conference on Technologies for Homeland Security.2007:95.
[7]王琛,黄莹.基于GPS的罗兰C系统定位误差修正研究[J].全球定位系统,2004,(06)：18.
[8]张峰云,柯希林,陈洪卿.长波定时ASF修正的GIS软件[J].宇航计测技术,2007,(03)：38.
[9]ITU-R. Electrical Characteristics of the Suface of the Earth[R]. ITU-R Reports,1990:527-530.
[101 ITU. PN Series Volume Geneva[R]. ITU-R Recommendations,1994:150-202.
[11]CCIR. Propagation in Non-ionized Media Geneva[R]. CCIR Recommendations and Reports, 1982:57-79,83-92,456.
[12]ITU-R. World Atlas of Ground Conductivities[R]. ITU Radiocommunication Sector,1999:832.
[13]IEEE Std 356-1974. IEEE guide for radio methods of measuring earth conductivity[S].:1974
[14]陈洪卿,柯希林.中国大地电导率电子地图的研制[J].陕西天文台台刊,2001,(02)：132.
[15]SJ 20839-2002.长波地波传输信道计算方法[S].中华人民共和国电子行业军用标准：2002
[16]熊皓.无线电波传播[M].北京：电子工业出版社,2002：115,331.
[17]杨迎春.一种高分辨率的罗兰C接收机天波延迟估计技术[J].计算机仿真,2006,23(7).
[18]Kawano T., Ishihara T. Ground wave propagation over land-to-sea and urban-to-suburban mixed-paths with inhomogeneous impedance surface[C]. Antennas and Propagation Society International Symposium,2005 IEEE.2005:375-378.
[19]莫愿斌刘贺同.智能优化算法的综述教学研究[J].科技创新报道,2008,13.
[20]王凌.智能优化算法及其应用[M].北京：清华大学出版社,2004：
[21]Wait James R. The Ancient and Modern History of EM Ground-Wave Propagation[J]. Ieee Antennas And Propagation Magazine,1998,5(40):7-24.
[22]高锐.罗兰C信号传播中的ASF修正方法[J].现代电子技术,2006,(13)：15-16.
[23]蒋腾旭.智能优化算法概述[J].电脑知识与技术,2008,.
[24]Weile D. S. Reviews and Abstracts [review of Genetic Algorithms in Electromagnetics (Haupt, R.L. and Werner, D.H.; 2007][J].2008,50(2):150-151.
[25]王小平,曹立明.遗传算法——理论、应用与软件实现[M].西安：西安交通大学出版社,2005：
[26]Meng Fan-lin, Wu Shun-xiang. Research of Genetic Algorithm in function optimization based on HCI[C]. IT in Medicine and Education,2008. ITME 2008. IEEE International Symposium on. 2008:1049-1052.
[27]李锋.全局优化的进化算法[D].西安电子科技大学,2007：62.
[28]Kumar A. A Novel Genetic Algorithm Approach to Solve Map Colour Problem[C]. Emerging Trends in Engineering and Technology,2008. ICETET'08. First International Conference on. 2008:288.
[29]Xianghui Deng. Application of Adaptive Genetic Algorithm in Inversion Analysis of Permeability Coefficients[C]. Genetic and Evolutionary Computing,2008. WGEC'08. Second International Conference on.2008:61.
[30]Ge Hong, Mao Zong-Yuan. Immune algorithm[C]. Proceedings of the 4th World Congress on Intelligent Control and Automation.2002:1784.
[31]张剑,谢学科,何华灿,et al.免疫计算的主要模型[J].微电子学与计算机,2004, (10).
[32]焦李成.免疫优化计算、学习与识别[M].北京：科学出版社,2006：
[33]肖人彬,王磊.人工免疫系统：原理、模型、分析及展望[J].计算机学报,2002,(12).
[34]李涛.计算机免疫学[M].北京：电子工业出版社,2004：
[35]Timmis Jon. Artificial immune systems-today and tomorrow[J]. Nat Comput,2007,6.
[36]Chunhua Li, Yanfei Zhu, Zongyuan Mao. A novel artificial immune algorithm applied to solve optimization problems[C]. ICARCV 2004 8th Control, Automation, Robotics and Vision Conference,2004.2004:232.
[37]Jang-Sung Chun, Hyun-Kyo Jung, Song-Yop Hahn. A study on comparison of optimization performances between immune algorithm and other heuristic algorithms[J]. IEEE Transactions on Magnetics,1998,34(5):2972.
[38]Li Maojun, Tang Zhong. An artificial immune algorithm based on bidding of power market[C]. International Conference on Power System Technology,2002.2002:2405.
[39]K.C.Tan C. K. Goh. An evolutionary artificial immune system for multi-objective optimization[J]. European Journal Of Operational Research,2007,.
[40]彭灵翔.改进的实数编码遗传算法解微分方程数值解[D].四川大学,2007：
[41]米顺强.遗传算法的有关改进及应用[D].解放军信息工程大学,2004：50.
[42]刘嵩.改进的并行遗传算法应用研究[D].大连交通大学,2006：77.
[43]江斌.人工免疫算法的基础研究及其应用[D].中南大学,2008：93.
[44]王孙安.混沌免疫优化组合算法[J].控制与决策,2006,21(2).
[45]张楠.基于混沌理论的免疫遗传算法[J].计算机应用,2006,26(5).
[46]熊虎岗.基于混沌免疫混合算法的多目标无功优化[J].电网技术,2007,31(11).
[2]吴海涛.罗兰_C最新进展及其对我国相关系统改造的启示[J].陕西天文台台刊,2000,23(1)：10-18.
[3]陈秀明.罗兰C组合导航中地波ASF修正的研究[D].陕西.西安：2007：6-13,27-35.
[4]Basker S., Williams P., Bransby M., et al. Enhanced Loran:Real-time maritime trials[C]. Position, Location and Navigation Symposium,2008 IEEE/ION.2008:792.
[5]袁海波,陈洪卿,王宏远.北斗长河组合导航伪时差测量与ASF修正[J].宇航计测技术,2004,(03)：5-7.
[6]Johnson G. W., Swaszek P. F., Hartnett R. J., et al. An Evaluation of eLoran as a Backup to GPS[C]. IEEE Conference on Technologies for Homeland Security.2007:95.
[7]王琛,黄莹.基于GPS的罗兰C系统定位误差修正研究[J].全球定位系统,2004,(06)：18.
[8]张峰云,柯希林,陈洪卿.长波定时ASF修正的GIS软件[J].宇航计测技术,2007,(03)：38.
[9]ITU-R. Electrical Characteristics of the Suface of the Earth[R]. ITU-R Reports,1990:527-530.
[101 ITU. PN Series Volume Geneva[R]. ITU-R Recommendations,1994:150-202.
[11]CCIR. Propagation in Non-ionized Media Geneva[R]. CCIR Recommendations and Reports, 1982:57-79,83-92,456.
[12]ITU-R. World Atlas of Ground Conductivities[R]. ITU Radiocommunication Sector,1999:832.
[13]IEEE Std 356-1974. IEEE guide for radio methods of measuring earth conductivity[S].:1974
[14]陈洪卿,柯希林.中国大地电导率电子地图的研制[J].陕西天文台台刊,2001,(02)：132.
[15]SJ 20839-2002.长波地波传输信道计算方法[S].中华人民共和国电子行业军用标准：2002
[16]熊皓.无线电波传播[M].北京：电子工业出版社,2002：115,331.
[17]杨迎春.一种高分辨率的罗兰C接收机天波延迟估计技术[J].计算机仿真,2006,23(7).
[18]Kawano T., Ishihara T. Ground wave propagation over land-to-sea and urban-to-suburban mixed-paths with inhomogeneous impedance surface[C]. Antennas and Propagation Society International Symposium,2005 IEEE.2005:375-378.
[19]莫愿斌刘贺同.智能优化算法的综述教学研究[J].科技创新报道,2008,13.
[20]王凌.智能优化算法及其应用[M].北京：清华大学出版社,2004：
[21]Wait James R. The Ancient and Modern History of EM Ground-Wave Propagation[J]. Ieee Antennas And Propagation Magazine,1998,5(40):7-24.
[22]高锐.罗兰C信号传播中的ASF修正方法[J].现代电子技术,2006,(13)：15-16.
[23]蒋腾旭.智能优化算法概述[J].电脑知识与技术,2008,.
[24]Weile D. S. Reviews and Abstracts [review of Genetic Algorithms in Electromagnetics (Haupt, R.L. and Werner, D.H.; 2007][J].2008,50(2):150-151.
[25]王小平,曹立明.遗传算法——理论、应用与软件实现[M].西安：西安交通大学出版社,2005：
[26]Meng Fan-lin, Wu Shun-xiang. Research of Genetic Algorithm in function optimization based on HCI[C]. IT in Medicine and Education,2008. ITME 2008. IEEE International Symposium on. 2008:1049-1052.
[27]李锋.全局优化的进化算法[D].西安电子科技大学,2007：62.
[28]Kumar A. A Novel Genetic Algorithm Approach to Solve Map Colour Problem[C]. Emerging Trends in Engineering and Technology,2008. ICETET'08. First International Conference on. 2008:288.
[29]Xianghui Deng. Application of Adaptive Genetic Algorithm in Inversion Analysis of Permeability Coefficients[C]. Genetic and Evolutionary Computing,2008. WGEC'08. Second International Conference on.2008:61.
[30]Ge Hong, Mao Zong-Yuan. Immune algorithm[C]. Proceedings of the 4th World Congress on Intelligent Control and Automation.2002:1784.
[31]张剑,谢学科,何华灿,et al.免疫计算的主要模型[J].微电子学与计算机,2004, (10).
[32]焦李成.免疫优化计算、学习与识别[M].北京：科学出版社,2006：
[33]肖人彬,王磊.人工免疫系统：原理、模型、分析及展望[J].计算机学报,2002,(12).
[34]李涛.计算机免疫学[M].北京：电子工业出版社,2004：
[35]Timmis Jon. Artificial immune systems-today and tomorrow[J]. Nat Comput,2007,6.
[36]Chunhua Li, Yanfei Zhu, Zongyuan Mao. A novel artificial immune algorithm applied to solve optimization problems[C]. ICARCV 2004 8th Control, Automation, Robotics and Vision Conference,2004.2004:232.
[37]Jang-Sung Chun, Hyun-Kyo Jung, Song-Yop Hahn. A study on comparison of optimization performances between immune algorithm and other heuristic algorithms[J]. IEEE Transactions on Magnetics,1998,34(5):2972.
[38]Li Maojun, Tang Zhong. An artificial immune algorithm based on bidding of power market[C]. International Conference on Power System Technology,2002.2002:2405.
[39]K.C.Tan C. K. Goh. An evolutionary artificial immune system for multi-objective optimization[J]. European Journal Of Operational Research,2007,.
[40]彭灵翔.改进的实数编码遗传算法解微分方程数值解[D].四川大学,2007：
[41]米顺强.遗传算法的有关改进及应用[D].解放军信息工程大学,2004：50.
[42]刘嵩.改进的并行遗传算法应用研究[D].大连交通大学,2006：77.
[43]江斌.人工免疫算法的基础研究及其应用[D].中南大学,2008：93.
[44]王孙安.混沌免疫优化组合算法[J].控制与决策,2006,21(2).
[45]张楠.基于混沌理论的免疫遗传算法[J].计算机应用,2006,26(5).
[46]熊虎岗.基于混沌免疫混合算法的多目标无功优化[J].电网技术,2007,31(11).
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