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Segmentazione di oggetti con tecniche di region growing e analisi delle componenti principali
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Kinematic GPS batch processing, a source for large sparse problems
No full textIn kinematic observation processing the equivalence between the state space approach (Kalman filtering plus smoothing) and the least squares approach including dynamic has been shown (Sansò et al., 2006). We will specialize the proposed batch solution (least squares including dynamic), considering the case of discrete-time lin-ear systems with constant biases, a case of practical interest in geodetic applications. A discrete-time lin-ear system leads often to large sparse matrices, and we need efficient matrix operation routines and efficient data structure to store them. Finally, con-stant biases are estimated using domain decomposition methods. Simulated and real data examples of the technique will be given for ki-nematic GPS data processin
Multi base kinematic GPS processing, a constrained batch solution applied to antenna array
No full textKinematic GPS Batch Processing, improving ambiguity fixing performances
No full textHas 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
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