Kinematic GPS Batch Processing, improving ambiguity fixing performances

Has been demonstrated that, alternatively to a Kalman filtering plus Kalman smoothing solu-tion can be used a different approach, called batch solution by the authors (Albertella et al., 2006). This method, with some algebraic expedient, allows obtaining least squares solutions equivalent to Kalman solutions with a comparable computational load. Has been numerically shown by the authors the equivalence between the state space approach and the batch solu-tion. We moved from the proposed geodetic solution, to study discrete-time linear systems with constant biases, a case of practical interest to estimate the integer ambiguities in carrier phase observations and their variance covariance matrix (Roggero, 2006). An application of the technique to real data will be given for kinematic GPS data processing, where float ambiguities are estimated via Schur decomposition, and where system dynamic strengthen ambiguities fixing, performed by LAMBDA method. The improvements in ambi-guity fixing performances will be shown formally and numerically, through the ambiguity di-lution of precision (ADOP), the dimension of the search space and the success rate.