Sect_8.3.3

8.3.3 Ambiguity Resolution: The Key to Modern GPS Surveying

APRIORI VALUES OF THE AMBIGUITY PARAMETERS

There are several possible sources for apriori ambiguity values (section 8.2.2):

Determine ambiguities with the aid of pseudo-range data (for example, via eqn (8.2-1)). The challenge is to average out the pseudo-range noise and multipath signal.
Determine ambiguities using linear combinations of dual-frequency phase and pseudo-range data (for example, the "four observable" combination -- section 6.4.7). The weakness is always the noise and multipath biases in the pseudo-range data.
Estimate ambiguities using linear combinations of dual-frequency phase data in Least Squares solution schemes, and then extract the L1 and L2 ambiguities using linear combinations of the other estimated ambiguities (section 8.4.4). This will give good apriori ambiguities and their uncertainties.
An approximate baseline estimate can be used to infer the likely range of ambiguities (taking into account the uncertainty in the baseline components). Baseline components may be estimated from triple-differenced phase solutions, or double-differenced pseudo-range solutions, or predicted by a Kalman filter (particularly useful in kinematic applications in which the baseline changes with time).
Apriori ambiguities may be obtained from an ambiguity-free solution. For short observation sessions the estimated ambiguities and their uncertainties define a volume containing candidate ambiguity sets that must be searched.

Note that the above procedures may give the likely ambiguities directly, or merely define candidate ambiguity sets that must be searched. The procedures may be applied to one epoch of data or take into account the data from the whole observation session. They may also be implemented in either the kinematic (moving antenna) mode, or the static baseline mode.

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