Real-time total system error estimation: Modeling and application in required navigation performance

In required navigation performance（RNP）, total system error（TSE） is estimated to provide a timely warning in the presence of an excessive error. In this paper, by analyzing the underlying formation mechanism, the TSE estimation is modeled as the estimation fusion of a fixed bias and a Gaussian random variable. To address the challenge of high computational load induced by the accurate numerical method, two efficient methods are proposed for real-time application, which are called the circle tangent ellipse method（CTEM） and the line tangent ellipse method（LTEM）,respectively. Compared with the accurate numerical method and the traditional scalar quantity summation method（SQSM）, the computational load and accuracy of these four methods are extensively analyzed. The theoretical and experimental results both show that the computing time of the LTEM is approximately equal to that of the SQSM, while it is only about 1/30 and 1/6 of that of the numerical method and the CTEM. Moreover, the estimation result of the LTEM is parallel with that of the numerical method, but is more accurate than those of the SQSM and the CTEM. It is illustrated that the LTEM is quite appropriate for real-time TSE estimation in RNP application.

Research on FMC analytic algorithm of PBN track transition coding

Airborne navigation database(NavDB)coding directly affects the result of analysis on the instrument flight procedure by the modern aircraft flight management computer(FMC).A reasonable flight track transition mode can improve the track tracking accuracy and flight quality of the aircraft.According to the path terminator(PT)and track transition characteristics of the performance based navigation(PBN)instrument flight procedure and by use of the world geodetic system(WGS)-84 ellipsoidal coordinate system,the algorithms for“fly by”and“fly over”track transition connections are developed,together with the algorithms for coordinates of fix-to-altitude(FA)altitude termination point and heading-to-an-intercept(VI)track entry point and for track transition display of the navigation display(ND).According to the simulation carried out based on the PBN instrument approach procedure coding of a certain airport and the PBN route data at a high altitude,the algorithm results are consistent with the FMC-calculated results and the actual ND results.

Lateral Flight Technical Error Estimation Model for Performance Based Navigation

Flight technical error （FTE） combined with navigation system error （NSE） is the main part of total system error （TSE） in performance based navigation （PBN）. The implementation of PBN requires pre-flight prediction and en-route short-term dynamical prediction of the TSE. Once the sum of predicted lateral FTE and NSE is greater than the specified PBN value, the PBN cannot operate. Thus, accurate modeling and thorough analysis of lateral FTE are indispensible. Multiple-input multiple-output （MIMO） lateral track control system of a transport aircraft is designed using linear quadratic Gaussian and loop transfer recovery （LQG/LTR） method, and the lateral FTE of a turbulence disturbed approach operation is analyzed. The error estimation mapping function of latera FTE and its bound estimation algorithm are proposed based on singular value theory. According to the forming mechanism of lateral FTE, the algorithm considers environmental turbulence fluctuation disturbance, aircraft dynamics and con- trol system parameters. Real-data-based Monte-Carlo simulation validates the theoretical analysis of FTE. It also shows that FTE is mainly caused by turbulence fluctuation disturbance when automatic flight control system （AFCS） is engaged and would in- crease with escalating environmental turbulence intensity.

Ship performance and navigation data compression and communication under autoencoder system architecture

Modern vessels are designed to collect,store and communicate large quantities of ship performance and navigation information through complex onboard data handling processes.That data should be transferred to shore based data centers for further analysis and storage.However,the associated transfer cost in large-scale data sets is a major challenge for the shipping industry,today.The same cost relates to the amount of data that are transferring through various communication networks(i.e.satellites and wireless networks),i.e.between vessels and shore based data centers.Hence,this study proposes to use an autoencoder system architecture(i.e.a deep learning approach)to compress ship performance and navigation parameters(i.e.reduce the number of parameters)and transfer through the respective communication networks as reduced data sets.The data compression is done under the linear version of an autoencoder that consists of principal component analysis(PCA),where the respective principal components(PCs)represent the structure of the data set.The compressed data set is expanded by the same data structure(i.e.an autoencoder system architecture)at the respective data center requiring further analyses and storage.A data set of ship performance and navigation parameters in a selected vessel is analyzed(i.e.data compression and expansion)through an autoencoder system architecture and the results are presented in this study.Furthermore,the respective input and output values of the autoencoder are also compared as statistical distributions and sample number series to evaluate its performance.