8.1.3 GPS Baseline Processing

PROCESSING OF DIFFERENCED DATA



There are a number of comments that can be made with regards to the various differenced phase data solutions:



GPS phase data processing sequence.

	

 

Triple-Differenced Phase Solution


The following are some characteristics of triple-difference solutions:


The triple-differences solution algorithm :

Difference epoch data between-satellites, form double-differences.

Difference double-differences between epochs at some sample rate (for example, every 5th observation epoch), form triple-differences.

Assume all triple-difference observations are independent when forming Weight Matrix (no correlations taken into account), define P matrix.

Form Observation Equations, construct the A matrix.

Accumulate Normal Equations, scaled by the Weight Matrix ATPA.

At end of data set, invert Normal Matrix and obtain geodetic parameter solution, = (ATPA)-1.ATP .

Update parameters.

Optionally scan triple-difference residuals for cycle slips in double-difference observables.

	


Double-Differenced Phase Solution (Ambiguity-Free)


The following are some characteristics of double-differenced phase (ambiguity-free) solutions:

	

The double-difference solution algorithm:

Difference epoch data between-satellites, form double-differences.

Apply data reductions, such as a troposphere bias model.

Construct Weight Matrix (depending on whether correlations are to be taken into account), define the P matrix.

Form Observation Equations --> construct the A matrix.

Accumulate Normal Equations, scaled by the Weight Matrix ATPA.

At end of session, invert Normal Matrix and obtain geodetic and ambiguity parameter solution, = (ATPA)-1.ATP .

Update parameters.

Decide (a) iterate solution, or (b) iterate solution only after ambiguity resolution attempted.

	


Double-Differenced Phase Solution (Ambiguity-Fixed)


The following are some characteristics of double-differenced phase (ambiguity-fixed) solutions:

	

The ambiguity-fixed solution algorithm:

Difference epoch data between-satellites, form double-differences as before but without ambiguities as solve-for parameters.

Apply data reductions, such as a troposphere bias model.

Construct Weight Matrix (depending on whether correlations are to be taken into account), define the P matrix.

Form Observation Equations, construct the A matrix.

Accumulate Normal Equations, scaled by the Weight Matrix ATPA.

At end of session, invert Normal Matrix and obtain geodetic parameter solution, = (ATPA)-1.ATP .

Update parameters.

This process can be iterated to resolve other ambiguities until (a) all have been resolved (and "fixed" to integers), or (b) no more can be reliably resolved.

	

Once ambiguities have been resolved, the ambiguous phase measurements are converted to precise range observations. As in conventional GPS navigation, single epoch positioning is now possible and hence "carrier-range" observations are ideal for kinematic positioning applications.

	

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© Chris Rizos, SNAP-UNSW, 1999