gps復(fù)合時(shí)鐘分析畢業(yè)設(shè)計(jì)中英文翻譯

1、中文 中文 4350 4350 字外文原文 外文原文Hindawi Publishing CorporationInternational Journal of Navigation and ObservationVolume 2008, Article ID 261384,8 pagesdoi:10.1155/2008/261384Research ArticleGPS Composite Clock AnalysisJames R.

2、 WrightAnalytical Graphics, In c., 220 Valle y Creek Blvd, E x ton, PA 19341, USA Correspondence should be addressed to James R. Wright, jwright@agi.comReceived 30 June 2007; Accepted 6 November 2007Recommended by Demetr

3、ios MatsakisCopyright © 2008 James R. Wright. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, p

4、rovided the original work is properly cited.Abstract The GPS composite clock defines GPS time, the timescale used today in GPS operations. GPS time is illuminated by examination of its role in the complete estimation and

5、 control problem relative to UTC/TAI. The phase of each GPS clock is unobservable from GPS pseudorange measurements, and the mean phase of the GPS clock ensemble (GPS time) is unobservable. A new and useful obs e r vabil

6、it y definition is presented, together with new observabilit y theorems, to demonstrate explicitly that GPS time is unobservable. Simulated GPS clock phase and frequency deviations, and simulated GPS pseudorange measurem

7、ents, are used to understand GPS time in terms of Kalman filter estimation errors.1. INTRODUCTIONGPS time is created by processing GPS pseudorange measurements with the operational GPS Kalman filter. Brown [2]refers to t

8、he object created by the Kalman filter as the GPS composite clock, and to GPS time as the implicit ensemble mean phase of the GPS composite clock. The fundamental goal by the USAF and the USNO is to control GPS time to w

9、ithin a specified bound of UTC/TAI. (I refer to TAI/UTC understanding that UTC has an accumulated discontinuity (a sum of leap seconds) when compared to TAI. But unique two-way transformations between TAI paper by Zucca

10、and Tavella [19 ] in 2005.Except for FM flicker noise, this model captures the most significant physics for all GPS clocks. I simulate and validate GPS pseudorange measurements using simulated phase deviations and simula

11、ted frequency deviations, according toZucca and Tavella.4. KALMAN FILTERSI present my approach for the optimal sequential estimation of clock deviation states and their error covariance functions. Sequential state estima

12、tes are generated recursively from two multidimensional stochastic update functions, the time update (TU) and the measurement update(MU). The TU moves the state estimate and covariance forward with time, accumulating in

13、tegrals of random clock deviation process noise in the covariance. The MU is performed at a fixed measurement time where the state estimate and covariance are corrected with new observation information.The sequential est

14、imation of GPS clock deviations re-quires the development of a linear TU and nonlinear MU. The nonlinear MU must be linearized locally to enable application of the linear Kalman MU. Kalman’s MU derives from Sherman’s the

15、orem, Sherman’s theorem derives from Anderson’s theorem [1], and Anderson’s theorem derives from the Brunn-Minkowki inequality theorem . The theoretical foundation for my linearized MU derives from these theorems.4.1. In

16、itial conditionsInitialization of all sequential estimators requires the use of an initial state estimate column matrix and an intial state estimate error covariance matrix for time t0 0 | 0? ? 0 | 0 P.4.2. Linear TU an

17、d nonlinear MUThe simultaneous sequential estimation of GPS clock phase and frequency deviation parameters can be studied with the development of a linear TU and nonlinear MU for the clock state estimate subset. This is

18、useful to study clock parameter estimation, as demonstrated in Section 6 .Let denote an n × 1 column matrix of state estimate components, where the left i j|? ?subscript j denotes state epoch tj and the right subs

19、cript i denotes time-tag ti for the last observation processed, where i, j ∈{0, 1, 2, ...}.Let denote an associated n × n square i j P |symmetric state estimate error co-variance matrix (positive eigenvalues).4.2.