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基于GLR-NT的显著误差检测与数据协调
蒋余厂,刘爱伦
作者单位E-mail
蒋余厂 华东理工大学自动化系 jych1225@163.com 
刘爱伦 华东理工大学自动化系  
摘要:
介绍一种广义似然比法(generalized likelihood ratio, GLR)与节点检测法 (nodal test, NT)组合的显著误差检测和稳态数据协调方法。 充分发挥了GLR法和NT法的优点,采用逐次侦破、补偿校正的策略,避免了传统显著误差侦破方法中系数矩阵降秩问题,并且融入了测量变量的上下限约束,最终实现显著误差的侦破、识别、处理和测量数据的协调。仿真结果显示,该方法对多显著误差特别是误差幅度较小或出现节点大显著误差相互抵消的情况具有较好的性能,优于单独的GLR法和NT-MT法,一实例表明了算法的有效性。
关键词:  广义似然比法  节点检测法  显著误差检测  数据协调
DOI:
分类号:
基金项目:国家高技术研究发展计划(863计划)
Gross error detection and data reconciliation based on a GLR-NT combined method
jiangyuchang,liuailun
Abstract:
A GLR-NT combined method based on generalized likelihood ratio and nodal test is introduced for gross error detection and data reconciliation. The decrease of coefficient matrix rank was avoided and the gross error’s identification and processing, also the measurement data’s reconciliation were achieved by using a strategy of detecting and compensating in successive iteration with bounds constraint of measurement variables, making full use of the advantages of both generalized likelihood ratio and nodal test. The simulation results showed that the method could get better performance and was superior to both sole GLR method and NT-MT method for the system with more than one error, especially when the gross error’s magnitude was small or several biased stream were counteracted at the same node. Finally an actual example is provided to indicate the usefulness of the proposed method.
Key words:  generalized likelihood ratio  nodal test  gross error detection  data reconciliation

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