Static positioning determination using GPS navigation solution

Statistics – Computation

Scientific paper

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Scientific paper

The purpose of this work is to use the Kalman filter to estimate the positioning vector of a static landmark from the navigation solutions obtained by GPS receivers. The estimator purpose is to calculate the state vector based in a set of observations. Such a computational algorithm processes measurements to produce a minimum error estimation of a system using knowledge of the dynamic and the measurements, measurement errors, and informations about initial conditions. There are two basic methods in the state estimation process to estimate the state vector: 1) when the state is updated in a reference or epoch instant, using a block of observations obtained during a space of time and in this case it is called batch estimator and the Least Squares method is the most used; 2) when the state vector is updated after processing each observation, in this case, the process is called sequential or recursive estimator and the most commom examples are recursive Least Squares and Kalman filter. In this work the Kalman filter will be used because of its robustness in real time applications, recursive and sequential nature, without unnecessary storage of observations, as they can be processed while being collected, and the convergence is obtained while observations are processed. The dynamical model considers the state vector to be estimated and the dynamic noise is assumed to be a discrete white noise. The observations modeling considers the m observations vector collected, the matrix relating the observations to the state and the observations noise vector. Data were collected using two dual frequency Ashtech Z-12 GPS receivers of geodetic quality, placed at known reference landmarks. A comparison will be done between the estimated state vector and the landmarks geodetic coordinates which were previously surveyed by IBGE. This work aims at performing a preliminary investigation on real time positioning techniques, with can thereafter be extended to navigation of space vehicles, including more complex features of non linear dynamic and the measurements.

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