基于高斯过程的飞行转弯区一致性监视技术研究
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摘要
针对导航误差和监视误差等干扰因素以及飞行转弯固有的不确定性和可变性,比如转弯开始点和转弯半径的差异,造成飞行转弯区内难以判定航空器是否与飞行计划或管制指令一致的问题,提出了一种比较航空器系统状态预期值和实际测量值的一致性监视方法,该方法基于高斯过程对转弯区的一致性飞行建模,通过模型求解出预期的状态值范围,其中引入附加的偏离项使模型有效地描述了飞行转弯区各个时段内系统状态值的合理变化趋势。系统状态预期值的计算通过高斯过程的贝叶斯推理框架完成,并且使用随机采样的方法解决引入偏离项后的后验概率无法求取解析解的问题。构建的飞行模型使用历史飞行数据进行训练,避免了直接依据飞行计划或管制指令计算系统状态预期值的方式下容易出现的问题,主要是无法充分考虑实际情况下各种因素对于系统状态值的影响。使用实际飞行场景中的监视系统数据进行测试,结果表明该方法计算得出的系统状态预期值区间反映了系统状态值的合理变化范围,能够有效判定一致性飞行。此外,相比于基于轨迹偏差阈值的判定方法,该方法能够取得更优的误报率和检测时间,避免了人为设置阈值不合理造成的影响,同时体现了该方法易于扩展,便于利用监视系统中位置信息之外的测量值提高一致性监视的性能。
In order to solve the problem of conformance monitoring during turns in which the disturbances, inherent uncertainties and variabilities,such as navigation errors,monitoring system errors, and differences of turning point and turning radius, make it difficult to determine whether an aircraft conforms to the assigned flight plan or the issued command, an approach based on the comparison between the expected state values and the measured state values was proposed. The interval of expected state values was calculated through modeling the conforming flights using Gaussian Processes. The deviation term was introduced to enable the model to describe the reasonable change of state values at different time points during turns. The calculation was completed under the framework of Bayesian inference in Gaussian Processes, and stochastic sampling was used to compute the posterior distribution which is analytically intractable after introducing deviation term. The proposed model for conforming flights was trained by the historical flight data. It can avoid common problems when the expected state values were inferred directly from flight plan or control command, mainly including the failure to account for the influences caused by various factors in practical situations. The experiments using flight data from realistic surveillance system showed that the calculated interval of expected state values in the proposed model reflects the reasonable range of state values and therefore is capable of monitoring conforming flights. Furthermore, compared with the conformance monitoring based on threshold value of trajectory deviation, this proposed approach achieved better false alarm rate and detection time and avoid the performance issues caused by improper settings of threshold value. The results also showed that this proposed approach has extensibility and can improve the performance of conformance monitoring by using more state values in addition to position information.
In order to solve the problem of conformance monitoring during turns in which the disturbances, inherent uncertainties and variabilities,such as navigation errors,monitoring system errors, and differences of turning point and turning radius, make it difficult to determine whether an aircraft conforms to the assigned flight plan or the issued command, an approach based on the comparison between the expected state values and the measured state values was proposed. The interval of expected state values was calculated through modeling the conforming flights using Gaussian Processes. The deviation term was introduced to enable the model to describe the reasonable change of state values at different time points during turns. The calculation was completed under the framework of Bayesian inference in Gaussian Processes, and stochastic sampling was used to compute the posterior distribution which is analytically intractable after introducing deviation term. The proposed model for conforming flights was trained by the historical flight data. It can avoid common problems when the expected state values were inferred directly from flight plan or control command, mainly including the failure to account for the influences caused by various factors in practical situations. The experiments using flight data from realistic surveillance system showed that the calculated interval of expected state values in the proposed model reflects the reasonable range of state values and therefore is capable of monitoring conforming flights. Furthermore, compared with the conformance monitoring based on threshold value of trajectory deviation, this proposed approach achieved better false alarm rate and detection time and avoid the performance issues caused by improper settings of threshold value. The results also showed that this proposed approach has extensibility and can improve the performance of conformance monitoring by using more state values in addition to position information.
引文
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[10]Liang Haijun,Yang Hongyu,Xiao Chao,et al.Flight conflict detection algorithm based on the three dimensional coordinate system[J].Journal of Sichuan University:Engineering Science Edition,2013,45(2):88-93.[梁海军,杨红雨,肖朝,等.3维坐标系下的飞行冲突探测算法[J].四川大学学报:工程科学版,2013,45(2):88-93.]
[11]Oaks R D,Paglione M.Determination of lateral flight adherence in recorded air traffic data[C]//AIAA Guidance,Navigation,and Control Conference and Exhibit.Reston,VA:AI-AA,2005:1-11.
[12]Williams C K I,Rasmussen C E.Gaussian processes for machine learning[M].Cambridge:MIT Press,2006.
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[14]Murphy K P.Machine learning:A probabilistic perspective[M].Cambridge:MIT Press,2012.
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[16]Neal R M.Monte Carlo implementation of gaussian process models for bayesian regression and classification[R].Toronto:University of Toronto,1997.
[2]Reynolds T G,Hansman R J.Analyzing conformance monitoring in air traffic control using fault detection approaches&operational data[C]//AIAA Guidance,Navigation,and Control Conference and Exhibit.Reston,VA:AIAA,2003:244-249.
[3]Reynolds T G,Hansman R J.Investigating conformance monitoring issues in air traffic control using fault detection techniques[J].Journal of Aircraft,2005,42(5):1307-1317.
[4]Seah C E,Aligawesa A,Hwang I.Algorithm for conformance monitoring in air traffic control[J].Journal of Guidance Control&Dynamics,2015,33(2):500-509.
[5]Zheng Q M,Zhao Y J.Probabilistic approach to trajectory conformance monitoring[J].Journal of Guidance Control Dynamics,2012,35(6):1888-1898.
[6]He Wenhong.Research on conformance monitoring technology in air traffic control[J].Computer&Digital Engineering,2008,36(1):48-51.[贺文红.空中交通管制一致性监视技术研究[J].计算机与数字工程,2008,36(1):48-51.]
[7]Peng Wei,Huang Rongshun,Tang Bin,et al.An airport sur-face target static/motion states test method applied by MLAT surveillance system[J].Journal of Sichuan University:Engineering Science Edition,2015,47(6):84-90.[彭卫,黄荣顺,唐斌,等.一种机场场面MLAT监视系统的目标状态检验方法[J].四川大学学报:工程科学版,2015,47(6):84-90.]
[8]Mou Qifeng,Feng Xiaolei,Xiang Shuoling.Design and realization of conflict detection alarm system for airport surface[J].Journal of Sichuan University:Engineering Science Edition,2015,47(4):104-110.[牟奇锋,冯晓磊,向硕凌.机场场面冲突检测告警系统的设计与实现[J].四川大学学报:工程科学版,2015,47(4):104-110.]
[9]Prandini M,Hu J,Lygeros J,et al.A probabilistic approach to aircraft conflict detection[J].IEEE Transactions on Intelligent Transportation Systems,2001,1(4):199-220.
[10]Liang Haijun,Yang Hongyu,Xiao Chao,et al.Flight conflict detection algorithm based on the three dimensional coordinate system[J].Journal of Sichuan University:Engineering Science Edition,2013,45(2):88-93.[梁海军,杨红雨,肖朝,等.3维坐标系下的飞行冲突探测算法[J].四川大学学报:工程科学版,2013,45(2):88-93.]
[11]Oaks R D,Paglione M.Determination of lateral flight adherence in recorded air traffic data[C]//AIAA Guidance,Navigation,and Control Conference and Exhibit.Reston,VA:AI-AA,2005:1-11.
[12]Williams C K I,Rasmussen C E.Gaussian processes for machine learning[M].Cambridge:MIT Press,2006.
[13]Seeger M.Gaussian processes for machine learning[J].International Journal of Neural Systems,2004,14(02):69-106.
[14]Murphy K P.Machine learning:A probabilistic perspective[M].Cambridge:MIT Press,2012.
[15]Goldberg P W,Williams C K I,Bishop C M.Regression with input-dependent noise:a Gaussian process treatment[C]//Conference on Advances in Neural Information Processing Systems.Cambridge:MIT Press,1997:493-499.
[16]Neal R M.Monte Carlo implementation of gaussian process models for bayesian regression and classification[R].Toronto:University of Toronto,1997.