| 基于遗传算法的模糊卡尔曼滤波在车辆导航中的运用 |
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| 引用本文： | 杨易,谷正气,胡林,罗国青,容哲.基于遗传算法的模糊卡尔曼滤波在车辆导航中的运用[J].中国机械工程,2006(Z2). |
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| 作者姓名： | 杨易 谷正气 胡林 罗国青 容哲 |
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| 作者单位： | 湖南大学汽车车身先进设计制造国家重点实验室，湖南大学汽车车身先进设计制造国家重点实验室，湖南大学汽车车身先进设计制造国家重点实验室，湖南大学汽车车身先进设计制造国家重点实验室，湖南大学汽车车身先进设计制造国家重点实验室 长沙，410082，长沙，410082，长沙，410082，长沙，410082，长沙，410082 |
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| 基金项目： | 湖南省自然科学基金(01JJY2081)，教育部优秀青年教师资助计划资助项目(2003355) |
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| 摘 要： | 针对常规联合卡尔曼滤波存在的缺陷,提出基于遗传优化的模糊卡尔曼滤波方法,并运用于全球定位系统(GPS)/轨迹推算系统(DR)汽车组合导航的数据融合中。采用模糊逻辑自适应控制器对联合卡尔曼滤波器的噪声方差和信息分配系数进行在线自适应调整,避免了子滤波器的发散,保持了全局估计的高精度。同时,利用改进的遗传算法来优化模糊控制器的隶属函数,避免了以往完全凭经验获取隶属函数参数的缺陷。通过对GPS/DR组合导航系统的仿真和实验,验证了上述算法的可用性和有效性。
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| 关 键 词： | 联合卡尔曼滤波 模糊控制 遗传算法 隶属函数 |
A Fuzzy Kalman Filter Based on Genetic Algorithm for Vehicle Navigation System |
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| Yang Yi Gu Zhengqi Hu Lin Luo Guoqing Rong Zhe State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University,Changsha.A Fuzzy Kalman Filter Based on Genetic Algorithm for Vehicle Navigation System[J].China Mechanical Engineering,2006(Z2). |
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| Authors: | Yang Yi Gu Zhengqi Hu Lin Luo Guoqing Rong Zhe State Key Laboratory of Advanced Design and Manufacture for Vehicle Body Hunan University Changsha |
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| Affiliation: | Yang Yi Gu Zhengqi Hu Lin Luo Guoqing Rong Zhe State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University,Changsha,410082 |
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| Abstract: | In order to overcome the disadvantages of regular federated Kalman filter,a fuzzy Kal- man filter based on genetic algorithm was presented and applied in information fusion of the GPS/DR vehicle integrated navigation system.The noise' covariance and information distribution coefficient of local filtering were modified online by the fuzzy logic adaptive controller in order to module Kalman filtering to be optimal and to improve the positioning accuracy of the integrated navigation system. The acquisition of membership function of fuzzy controller usually relies to a great extent on empirical and heuristic knowledge.A kind of CGA was used to optimize fuzzy membership function and obtain the optimal or sub-optimal control rules.The simulation results demonstrate the feasibility and ef- fectiveness of the method. |
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| Keywords: | federated Kalman filter fuzzy control genetic algorithm membership function |
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