GNSS接收机自主完备性监测算法研究
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摘要
随着GPS现代化和俄罗斯的GLONASS补星计划的实施和推进,欧洲委员会和欧洲航天局也在积极筹备和实现Galilieo导航系统。当多个系统联合导航时,发生错误观测的可能性也增加了,因此人们在研究提高GNSS导航定位精度的同时,对GNSS导航定位结果的质量控制也进行了大量的研究。接收机的自主完备性监测(Receiver Autonomous Integrity Monitoring)仅靠接收机接收到的冗余观测信号的自检较来完成对定位结果的质量控制,由于RAIM不需要昂贵的外部设施,而且不受地域范围的限制同时能兼顾到接收机端可能发生的观测错误,目前成为GNSS领域的研究热点之一。
最初的RAIM是GPS为飞机导航的完备性监测的辅助手段。自上世纪80世纪以来,许多学者对GPS导航系统下的RAIM'性能和算法做了大量的研究和分析。研究的焦点是提高RAIM的可用性和探测并剔除错误观测,从而保证GNSS导航的安全性能。
本论文就RAIM作为一般用户的完备性监测手段为出发点,研究了RAIM的现有算法和存在的问题,提出了基于w—检验确定RAIM的定位保护级方法,计算分析了GPS/GLONASS系统下的RAIM性能,提出将预测残差新息序列作为RAIM粗差检测和识别的方法,并用实测数据验证了预测残差新息序列法优于传统的RAIM粗差检测和识别方法。本论文的研究内容如下：
1、研究了RAIM定位保护水平的算法,提出将基于w—检验来确定RAIM定位保护级的算法,并提出将可区分性作为RAIM考虑的性能之一。
2、模拟场景分析了误警和漏检概率、观测精度和卫星的几何结构对RAIM定位保护级和相关系数的影响,计算了当可用性为99.9%时,全球GNSS用户的RAIM定位保护级和相关系数,计算结果表明,新算法的RAIM可用性高于传统算法的可用性,当误警和漏检概率分布3.33×10-7，0.1%时,在GPS/GLONASS系统下,全球绝大部分用户平面上的定位保护级在12米以下,高程方向上的保护级在20米以下,
3、分析了在平差系统中,当函数模型存在偏差时,最小二乘估计和Kalman滤波及其残差的统计特性。分析推导了Kalman滤波的最小可检测粗差和不可检测的粗差对滤波的影响以及识别粗差是假设检验量之间的相关系数的计算公式。
4、提出将新息序列检测法作为RAIM粗差检测和识别的基本算法。从实际误警率、粗差探测率、识别粗差正确率、定位结果的可用性和定位精度全面地分析了传统的向前-向后粗差探测法、基于相关分析的向前-向后法和新息序列粗差检测法在不同导航系统下对单一粗差和双粗差的检测效果,用静态和动态GPS/GLONASS观测数据验证了将新息序列检测法作为RAIM粗差检测和识别的基本算法的可行性。
With modernization of GPS and implement of Russia's full constellation of GLONASS, the development of Galileo by the European Commission and European Space is continuing. The positioning accuracy will be significantly improved due to the future multi-constellation and multi-frequency. Achieving the high positioning accuracy, one pays more attention to the positioning quality. The quality control can be accomplished by GNSS user end such as Receiver Autonomous Integrity Monitoring (RAIM). RAIM refers to integrity monitoring of GNSS navigation signals performed by receiver independent of external reference systems, except of the navigation signal themselves. With lots of advantages such as no need of external facilities such as Ground-Based Augmentation Systems (GBAS) or Space-Based Augmentation Systems (SBAS) and concerning receiver around obstacles, RAIM draw attentions and significant efforts have been made to develop and analysis GNSS RAIM methods and algorithms over the past decades.
While existing RAIM techniques, generally based on single bias will not be justifiable of the next generation GNSS due to multiple biases occurring with increased probability. An ideal RAIM can detect and identify biases with both of low false alarm rate and missing detecting rate, and meanwhile, RAIM provides user the HPL and VPL (Horizontal and Vertical Protection Level).
This paper aims at improvement of RAIM algorithm for general GNSS user and focuses on evaluating RAIM performance based on Protection Level and Separability. Besides, the paper mainly seeks the algorithm for fault detection and identification for RAIM. The major contributions of this research are:
a) Statistic characteristics of estimates were derived by supposing unperceived model error. The internal reliability and external reliability of Kalman filter were derived. Besides, the correlation coefficient for statistic test of Innovation is conducted.
b) A new algorithm was proposed to calculate HPL and VPL for improving RAIM availability and Separability should be concerned for RAIM performance. Comprehensive analysis of GPS and GPS/GLONASS benefits with respect to RAIM was conducted. Some scenarios were performed to show how HPL, VPL and separability vary with satellite geometric, observation variance and probabilities of false alarm and missed dectection for single epoch, global snapshot and 24-hour temporal scenarios.
c) Innovation sequence detecting bias method was presented to detect and indentify bias. Innovation sequence detection verified by comparing with conventional forward-backward method and correlation-test based forward-backward method. The performances of different FDI method were evaluated by correct detection rate, false alarm rate, correct identification, availability and positioning accuracy after exclusion. The performances analysis showed innovation sequence detecting can properly detect and identify not only single bias but also double biases even under only GPS.
最初的RAIM是GPS为飞机导航的完备性监测的辅助手段。自上世纪80世纪以来,许多学者对GPS导航系统下的RAIM'性能和算法做了大量的研究和分析。研究的焦点是提高RAIM的可用性和探测并剔除错误观测,从而保证GNSS导航的安全性能。
本论文就RAIM作为一般用户的完备性监测手段为出发点,研究了RAIM的现有算法和存在的问题,提出了基于w—检验确定RAIM的定位保护级方法,计算分析了GPS/GLONASS系统下的RAIM性能,提出将预测残差新息序列作为RAIM粗差检测和识别的方法,并用实测数据验证了预测残差新息序列法优于传统的RAIM粗差检测和识别方法。本论文的研究内容如下：
1、研究了RAIM定位保护水平的算法,提出将基于w—检验来确定RAIM定位保护级的算法,并提出将可区分性作为RAIM考虑的性能之一。
2、模拟场景分析了误警和漏检概率、观测精度和卫星的几何结构对RAIM定位保护级和相关系数的影响,计算了当可用性为99.9%时,全球GNSS用户的RAIM定位保护级和相关系数,计算结果表明,新算法的RAIM可用性高于传统算法的可用性,当误警和漏检概率分布3.33×10-7，0.1%时,在GPS/GLONASS系统下,全球绝大部分用户平面上的定位保护级在12米以下,高程方向上的保护级在20米以下,
3、分析了在平差系统中,当函数模型存在偏差时,最小二乘估计和Kalman滤波及其残差的统计特性。分析推导了Kalman滤波的最小可检测粗差和不可检测的粗差对滤波的影响以及识别粗差是假设检验量之间的相关系数的计算公式。
4、提出将新息序列检测法作为RAIM粗差检测和识别的基本算法。从实际误警率、粗差探测率、识别粗差正确率、定位结果的可用性和定位精度全面地分析了传统的向前-向后粗差探测法、基于相关分析的向前-向后法和新息序列粗差检测法在不同导航系统下对单一粗差和双粗差的检测效果,用静态和动态GPS/GLONASS观测数据验证了将新息序列检测法作为RAIM粗差检测和识别的基本算法的可行性。
With modernization of GPS and implement of Russia's full constellation of GLONASS, the development of Galileo by the European Commission and European Space is continuing. The positioning accuracy will be significantly improved due to the future multi-constellation and multi-frequency. Achieving the high positioning accuracy, one pays more attention to the positioning quality. The quality control can be accomplished by GNSS user end such as Receiver Autonomous Integrity Monitoring (RAIM). RAIM refers to integrity monitoring of GNSS navigation signals performed by receiver independent of external reference systems, except of the navigation signal themselves. With lots of advantages such as no need of external facilities such as Ground-Based Augmentation Systems (GBAS) or Space-Based Augmentation Systems (SBAS) and concerning receiver around obstacles, RAIM draw attentions and significant efforts have been made to develop and analysis GNSS RAIM methods and algorithms over the past decades.
While existing RAIM techniques, generally based on single bias will not be justifiable of the next generation GNSS due to multiple biases occurring with increased probability. An ideal RAIM can detect and identify biases with both of low false alarm rate and missing detecting rate, and meanwhile, RAIM provides user the HPL and VPL (Horizontal and Vertical Protection Level).
This paper aims at improvement of RAIM algorithm for general GNSS user and focuses on evaluating RAIM performance based on Protection Level and Separability. Besides, the paper mainly seeks the algorithm for fault detection and identification for RAIM. The major contributions of this research are:
a) Statistic characteristics of estimates were derived by supposing unperceived model error. The internal reliability and external reliability of Kalman filter were derived. Besides, the correlation coefficient for statistic test of Innovation is conducted.
b) A new algorithm was proposed to calculate HPL and VPL for improving RAIM availability and Separability should be concerned for RAIM performance. Comprehensive analysis of GPS and GPS/GLONASS benefits with respect to RAIM was conducted. Some scenarios were performed to show how HPL, VPL and separability vary with satellite geometric, observation variance and probabilities of false alarm and missed dectection for single epoch, global snapshot and 24-hour temporal scenarios.
c) Innovation sequence detecting bias method was presented to detect and indentify bias. Innovation sequence detection verified by comparing with conventional forward-backward method and correlation-test based forward-backward method. The performances of different FDI method were evaluated by correct detection rate, false alarm rate, correct identification, availability and positioning accuracy after exclusion. The performances analysis showed innovation sequence detecting can properly detect and identify not only single bias but also double biases even under only GPS.
引文
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