scholarly journals GPS Composite Clock Analysis

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
James R. Wright
Keyword(s):  
Kalman Filter ◽  
Control Problem ◽  
Estimation Errors ◽  
Clock Analysis ◽  
The Mean ◽  
Estimation And Control ◽  
And Control

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 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 observability definition is presented, together with new observability theorems, to demonstrate explicitly that GPS time is unobservable. Simulated GPS clock phase and frequency deviations, and simulated GPS pseudorange measurements, are used to understand GPS time in terms of Kalman filter estimation errors.

scholarly journals Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Roberta Veloso Garcia ◽  
Helio Koiti Kuga ◽  
Maria Cecilia F. P. S. Zanardi
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Attitude Determination ◽  
Unscented Kalman Filter ◽  
Artificial Satellite ◽  
Real Data ◽  
Nonlinear Functions ◽  
Estimation And Control ◽  
And Control

The aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite). The real-time estimator used in this work for attitude determination is the Unscented Kalman Filter. This filter is a new alternative to the extended Kalman filter usually applied to the estimation and control problems of attitude and orbit. This algorithm is capable of carrying out estimation of the states of nonlinear systems, without the necessity of linearization of the nonlinear functions present in the model. This estimation is possible due to a transformation that generates a set of vectors that, suffering a nonlinear transformation, preserves the same mean and covariance of the random variables before the transformation. The performance will be evaluated and analyzed through the comparison between the Unscented Kalman filter and the extended Kalman filter results, by using real onboard data.

scholarly journals Distributed Robust Kalman Filtering with Unknown and Noisy Parameters in Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Donghua Chen ◽  
Ya Zhang ◽  
Cheng-Lin Liu ◽  
Yangyang Chen
Keyword(s):  
Kalman Filter ◽  
Sensor Networks ◽  
Kalman Filtering ◽  
Estimation Error ◽  
Measuring System ◽  
Mean Square ◽  
System Matrix ◽  
Estimation Errors ◽  
System Parameters ◽  
The Mean

This paper investigates the distributed filtering for discrete-time-invariant systems in sensor networks where each sensor’s measuring system may not be observable, and each sensor can just obtain partial system parameters with unknown coefficients which are modeled by Gaussian white noises. A fully distributed robust Kalman filtering algorithm consisting of two parts is proposed. One is a consensus Kalman filter to estimate the system parameters. It is proved that the mean square estimation errors for the system parameters converge to zero if and only if, for any one system parameter, its accessible node subset is globally reachable. The other is a consensus robust Kalman filter to estimate the system state based on the system matrix estimations and covariances. It is proved that the mean square estimation error of each sensor is upper-bounded by the trace of its covariance. An explicit sufficient stability condition of the algorithm is further provided. A numerical simulation is given to illustrate the results.

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