基于鲁棒估计理论的列车组合定位方法研究
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摘要
摘要：列车定位是列车运行控制系统形成有效控制策略并保障列车安全高效运行的重要保证,采用多种传感器集成构成列车组合定位系统是列车定位技术的发展趋势。传感器数据融合作为列车组合定位系统的核心功能,在传感器数量增多、系统结构趋于复杂的情况下,需要充分考虑融合估计的鲁棒性,使定位系统在确保安全的前提下具备较高的性能水平。
论文针对列车定位的实际特性,结合多传感器组合的系统结构,从H∞鲁棒滤波这一类典型的鲁棒估计方法出发,研究了基于鲁棒估计理论的列车组合定位方法,提出一种cubature H∞鲁棒滤波算法,采用联邦滤波结构形成分散化滤波算法,并进一步提出采用变约束系数策略的白适应滤波方案。在此基础上,论文将研究拓展至列车组合定位的完整过程,研究了列车组合定位的完整性保障方法。论文的创新性工作主要包括：
(1)总结基于滤波估计的列车组合定位模型,提出一种cubature H∞非线性鲁棒滤波算法,并基于联邦滤波结构,提出了基于信息分配系数动态调节的分散化cubature H∞滤波方案。
(2)提出一种变约束系数cubature H∞自适应滤波算法,对参数自适应方案中的约束系数作用机理、最优约束系数求解等问题进行了研究,确定了可行的约束系数动态调节策略。
(3)提出一种分级递进式列车组合定位完整性保障方法,确定了系统自主完整性监测及保障框架,并针对列车组合定位的传感器输入、传感器融合、地图匹配输出等过程分别提出了完整性保障策略。
(4)构建了多传感器列车组合定位仿真系统,对传感器误差模型、列车轨迹仿真等问题进行了研究,通过设置不同场景进行的多传感器协同仿真对论文理论成果进行了集成验证。
论文结合列车组合定位仿真及实际现场测试实验,验证了本文提出的基于鲁棒估计理论的列车组合定位方法能够在多种性能指标中取得良好平衡,有效用于现代列车运行控制系统。
ABSTRACT: Train positioning is crucial for train control system to generate effective train control strategies and guarantee the safety and efficiency of train operation. Traditional train positioning system with the single-sensor configuration could not meet the developing requirements of modern train control system, while the implementation of multi-sensor based train integrated positioning system has become an inevitable trend. As multi sensors and more complicated architecture are adopted in train integrated positioning system, multi-sensor fusion, which is the core function of the integrated system, should be designed with more consideration to robustness of the filtering and estimation, so that a desired performance level could be realized as well as safety.
In this thesis, according to the practical characteristics of train positioning and structure of the integrated system, based on the H∞robust filtering method, the robust estimation theory based train integrated positioning method is studied, and a cubature H∞robust filtering algorithm is proposed. A federation structure based decentralized filtering strategy is presented for cubature H∞filter, and a variable restraint coefficient based adaptive filtering approach is developed. Furthermore, the present study expands the perspective to cover the complete procedure of train integrated positioning, and explores integrity assurance method for the train integrated positioning system.
The innovations of the thesis are as follows:
(1) The study summarizes the model of filtering estimation based train integrated positioning, proposes a cubature H∞nonlinear robust filtering algorithm, and presents an information distribution coefficient adaptive decentralized filtering solution for the cubature H∞filter, on the basis of a federation structure.
(2) The study, through the exploration of the restraint coefficient mechanism and solution for optimal restraint coefficient, presents a variable restraint coefficient based cubature H∞adaptive filtering approach, and determines a feasible dynamic adjustment strategy for the restraint coefficient.
(3) The study proposes a novel integrity assurance method for the train integrated positioning system, where an Autonomous Integrity Monitoring and Assurance (AIMA) scheme is established, and specific integrity assurance solutions are formed for the sensor collection stage, sensor fusion stage and map matching stage respectively.
(4) The study establishes a train integrated positioning simulation system, where the issues of sensor error models and train trajectory generation are solved, and realizes the validation of theoretical results in this thesis by sensor collaborative simulation under different scenarios.
In this thesis, simulations and field experiments are both employed for validation, which demonstrate that the proposed robust estimation theory based train integrated positioning method could reach a favorable balance between different performance indices of train positioning, and is applicable for modern train control systems.
论文针对列车定位的实际特性,结合多传感器组合的系统结构,从H∞鲁棒滤波这一类典型的鲁棒估计方法出发,研究了基于鲁棒估计理论的列车组合定位方法,提出一种cubature H∞鲁棒滤波算法,采用联邦滤波结构形成分散化滤波算法,并进一步提出采用变约束系数策略的白适应滤波方案。在此基础上,论文将研究拓展至列车组合定位的完整过程,研究了列车组合定位的完整性保障方法。论文的创新性工作主要包括：
(1)总结基于滤波估计的列车组合定位模型,提出一种cubature H∞非线性鲁棒滤波算法,并基于联邦滤波结构,提出了基于信息分配系数动态调节的分散化cubature H∞滤波方案。
(2)提出一种变约束系数cubature H∞自适应滤波算法,对参数自适应方案中的约束系数作用机理、最优约束系数求解等问题进行了研究,确定了可行的约束系数动态调节策略。
(3)提出一种分级递进式列车组合定位完整性保障方法,确定了系统自主完整性监测及保障框架,并针对列车组合定位的传感器输入、传感器融合、地图匹配输出等过程分别提出了完整性保障策略。
(4)构建了多传感器列车组合定位仿真系统,对传感器误差模型、列车轨迹仿真等问题进行了研究,通过设置不同场景进行的多传感器协同仿真对论文理论成果进行了集成验证。
论文结合列车组合定位仿真及实际现场测试实验,验证了本文提出的基于鲁棒估计理论的列车组合定位方法能够在多种性能指标中取得良好平衡,有效用于现代列车运行控制系统。
ABSTRACT: Train positioning is crucial for train control system to generate effective train control strategies and guarantee the safety and efficiency of train operation. Traditional train positioning system with the single-sensor configuration could not meet the developing requirements of modern train control system, while the implementation of multi-sensor based train integrated positioning system has become an inevitable trend. As multi sensors and more complicated architecture are adopted in train integrated positioning system, multi-sensor fusion, which is the core function of the integrated system, should be designed with more consideration to robustness of the filtering and estimation, so that a desired performance level could be realized as well as safety.
In this thesis, according to the practical characteristics of train positioning and structure of the integrated system, based on the H∞robust filtering method, the robust estimation theory based train integrated positioning method is studied, and a cubature H∞robust filtering algorithm is proposed. A federation structure based decentralized filtering strategy is presented for cubature H∞filter, and a variable restraint coefficient based adaptive filtering approach is developed. Furthermore, the present study expands the perspective to cover the complete procedure of train integrated positioning, and explores integrity assurance method for the train integrated positioning system.
The innovations of the thesis are as follows:
(1) The study summarizes the model of filtering estimation based train integrated positioning, proposes a cubature H∞nonlinear robust filtering algorithm, and presents an information distribution coefficient adaptive decentralized filtering solution for the cubature H∞filter, on the basis of a federation structure.
(2) The study, through the exploration of the restraint coefficient mechanism and solution for optimal restraint coefficient, presents a variable restraint coefficient based cubature H∞adaptive filtering approach, and determines a feasible dynamic adjustment strategy for the restraint coefficient.
(3) The study proposes a novel integrity assurance method for the train integrated positioning system, where an Autonomous Integrity Monitoring and Assurance (AIMA) scheme is established, and specific integrity assurance solutions are formed for the sensor collection stage, sensor fusion stage and map matching stage respectively.
(4) The study establishes a train integrated positioning simulation system, where the issues of sensor error models and train trajectory generation are solved, and realizes the validation of theoretical results in this thesis by sensor collaborative simulation under different scenarios.
In this thesis, simulations and field experiments are both employed for validation, which demonstrate that the proposed robust estimation theory based train integrated positioning method could reach a favorable balance between different performance indices of train positioning, and is applicable for modern train control systems.
引文
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