Kinematic GPS batch processing, improving ambiguity fixing performances

In kinematic observation processing the equivalence between the state space approach (Kalman filtering plus smmothing) and the last squares approach including dynamic has been shown (Albertellat al. 2006). we have specialized the proposed batch solution (least squares including dynamic), considering the case of discrete-time linear systems with constant biases, applying the algorithm to estimate the float ambiguities and their variance-covariance matrix in GPS observations (Roggero, 2006). The improvements in ambiguity fixing performances will be shown, through the ADOP, the dimension of the search space and the success rate.