At Flight and Systems test Saab AB, a post-processing software is used to process GPS data. A new software by the name GrafNav has been purchased and the purpose of this master thesis therefore became, partly to make a judgment regarding GrafNav’s ability to estimate position, velocity and accuracy, partly to if needed improve the estimate and finally find one or several methods to estimate the position and velocity accuracy. The judgment of GrafNav was performed partly by a comparison to the former post-processing software (PNAV) and partly by a comparison to the airplane’s inertial navigation system (INS). The experiments showed that GrafNav’s ability to estimate the position is comparable with PNAV:s, but its capacity to estimate the velocity is considerably worse. The velocity estimate even showed a more noisy behavior than the original velocity from the receiver. More effort is needed to judge GrafNav’s ability to estimate the accuracy thru its quality signals. A few trials were made to improve the velocity estimate thru Kalman filtering (Rauch-Tung-Striebel smoothing). The filtering was first made using only the position data from GrafNav as measurements and afterwards both position and velocity data from GrafNav was used. The outcome of the Kalman filtering showed that the best result is obtained when solely position data is used and that the estimate in general is comparable with PNAV:s estimate, but considerable big deviations is obtained in conjunction to interruptions in position data. More over, is more effort needed using both position and velocity data when performing the smoothing and also replacing the stationary Kalman filter with an adaptive filter. Finally a method was brought out to estimate the position precision and a method to estimate the velocity accuracy. Both methods use the INS velocity to perform an estimation.

<p>At Flight and Systems test Saab AB, a post-processing software is used to process GPS data. A new software by the name GrafNav has been purchased and the purpose of this master thesis therefore became, partly to make a judgment regarding GrafNav’s ability to estimate position, velocity and accuracy, partly to if needed improve the estimate and finally find one or several methods to estimate the position and velocity accuracy. </p><p>The judgment of GrafNav was performed partly by a comparison to the former post-processing software (PNAV) and partly by a comparison to the airplane’s inertial navigation system (INS). The experiments showed that GrafNav’s ability to estimate the position is comparable with PNAV:s, but its capacity to estimate the velocity is considerably worse. The velocity estimate even showed a more noisy behavior than the original velocity from the receiver. More effort is needed to judge GrafNav’s ability to estimate the accuracy thru its quality signals. </p><p>A few trials were made to improve the velocity estimate thru Kalman filtering (Rauch-Tung-Striebel smoothing). The filtering was first made using only the position data from GrafNav as measurements and afterwards both position and velocity data from GrafNav was used. The outcome of the Kalman filtering showed that the best result is obtained when solely position data is used and that the estimate in general is comparable with PNAV:s estimate, but considerable big deviations is obtained in conjunction to interruptions in position data. More over, is more effort needed using both position and velocity data when performing the smoothing and also replacing the stationary Kalman filter with an adaptive filter. </p><p>Finally a method was brought out to estimate the position precision and a method to estimate the velocity accuracy. Both methods use the INS velocity to perform an estimation.</p>