【作者】 李志强；

【导师】 苏志刚； 高益寰；

【作者基本信息】 中国民航大学 ， 电子与通信工程（专业学位）， 2017， 硕士

【摘要】 国际民航组织(International Civil Aviation Organization,ICAO)预测2050年航空排放将比2010年增长300%左右,因此,在国际气候变化谈判中,民用航空的节能减排成为前沿问题和各国斗争的焦点。民航节能减排需要有效的主动核查与监测手段,根据航空器不同飞行阶段的能耗模型,结合航空器轨迹信息,可以实现对航空排放的主动监测。因此,基于航迹信息的飞行阶段划分技术是实现航空排放主动监测任务的核心技术之一。首先,建立了航空器典型飞行过程的分段模型,提取能反映各个飞行阶段变化特点的飞行高度、地速和升降率参数作为阶段划分的特征参数。利用广播式自动相关监视(Automatic Dependent Surveillance-Broadcast,ADS-B)系统获得航空器的动态航迹信息,建立动态数据提取流程。其次,针对航空器实时轨迹的阶段划分问题,基于民航监视系统提供的航空器高度、地速和升降率等动态信息,提出采用顺序滑动双窗口数据的T~2统计量大小进行变点检测与估计的方法。通过T~2统计量是否超过检测门限来判别是否存在变点,并采用最大似然估计(Maximum Likelihood Estimation,MLE)方法确定变点的位置。最后,为改善变点检测方法的性能提出两种优化手段。一种是将影响T~2值的常系数归一化,消除窗口长度对T~2统计量的显著影响,以减少虚警;一种是利用协方差矩阵的对角加载方法来解决不同变量方差显著差距引起的T~2统计失配问题,使统计量更加稳健。B737-800航空器的不同航线数据验证方法是可行的。更多还原

【Abstract】 International Civil Aviation Organization(ICAO)predicted that aviation emissions in 2050 will be about 300% higher than that in 2010.As a result,civil aviation energy-saving and emission-reduction become the frontier issue and focus of national struggles in the international climate negotiations.Civil aviation energy-saving and emission-reduction require effective means of active verification and monitoring.According to the aircraft energy consumption model in different flight phases,as well as aircraft trajectories,active monitoring of aviation emissions can be realized.Therefore,flight phase segmentation based on aircraft trajectories is one of the core technologies to realize the active monitoring task of airline emissions.Firstly,the segmentation model of typical aircraft flight phases was established,and those parameters,such as the flight altitude,ground speed and lift rate were extracted,which reflected the changing characteristics of different flight phases.Aircraft trajectory information was obtained via the Automatic Dependent Surveillance-Broadcast(ADS-B)system,and a dynamic data extraction process was established.Secondly,aiming at the flight phase segmentation of real-time aircraft trajectory,based on the aircraft altitude,ground speed and lift rate and other dynamic information that provided by civil aviation surveillance system,a method which calculated the Hotelling’s T-square statistic of sequential double sliding window data was proposed for detecting and estimating the change points.A change point was detected when the Hotelling’s T-square statistic exceeded the threshold,followed by Maximum Likelihood Estimation(MLE)method,which was used to determine the position of the change point.Finally,two optimization methods were proposed to improve the performance of change point detection.On the one hand,by normalizing the constant coefficient of simplified Hotelling’s T-square expression,the significant effect of window length on Hotelling’s T-square statistic was eliminated,and false alarm reduced.On the other hand,diagonal loading method was used to solve the statistical mismatch problem caused by the significant gap of different variables’ variance,as well as making the Hotelling’s T-square statistic more robust.The proposed methods were validated effective via different airline data of B737-800 aircrafts.更多还原