El papel de las tecnologías de la información en los procesos de socialización

As explained in 3.3, carrier phase measurements can be affected by cycle slips, particularly when they come from a low-cost receiver in dynamic conditions. In order to avoid the problem of cycle slip detection and estimation, single-epoch ambiguity resolution was applied in the previous sections. As the ambiguity is re-initialized at each epoch, any change in the ambiguity value between epochs will not impact the estimation process. However, this method is sub-optimal, as cycle slips usually don’t occur at every epoch on every satellite.

In order to determine the improvement brought by the proposed cycle slip resolution, the same configuration, i.e. the baseline configuration with GLONASS inter-channel biases and proposed weighting scheme, is used. However, carrier phase ambiguities are now estimated as constants as described in section 4.2.3, provided cycle slips is successfully estimated as integers. Whenever a cycle slip is detected, it is estimated as an integer and added to the ambiguity state of the RTK Kalman filter to correct for its effect, as explained in section 3.3.

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6.3.1

Position Error on Data Set 1

The new positioning error results are plotted on Figure 6.20 and Figure 6.21.

Figure 6.20 Position error in downtown Toulouse (data set 1). Black asterisk represents epochs when ambiguity vector is validated

and fixed as integer

Figure 6.21 Position error on Toulouse’s beltway (data set 1). Black asterisk represents epochs when ambiguity vector is validated and fixed as integer

Position error has been reduced compared to the single-epoch approach, notably in urban environment where the solution is smoother. Indeed, as carrier phase measurements are associated to a very small variance, the Kalman filter tends to give them more weight while the ambiguity estimate gets more accurate. Assuming the ambiguity is constant increases the accuracy of the float ambiguity through time.

However, when very large multipaths are present during an extended period, position can still be offset by a few meters. This situation is visible when the vehicle remains static during a long time in a shadowed environment, due to traffic for instance. An example of ground track can be found on Figure 6.22, during an event occurring between 9:20 and 9:25. This event points out one of the limit of the proposed algorithm. Estimating carrier phase as constants increases the accuracy of the float ambiguity provided pseudorange error is zero mean over a short period of time. In the case of time-correlated multipath in a static environment, the solution can be biased and never converge. However, as the targeted application is a land vehicle, the environment is expected to be dynamic and the time correlation of pseudorange multipath short.

09:15 09:20 09:25 09:30 09:35 09:40 -10 -8 -6 -4 -2 0 2 4 6 8 10

Error in the estimated position (Downtown Toulouse)

Time of Day (hours:minutes)

m e te rs Up East North Fix mode 09:45 09:50 09:55 -10 -8 -6 -4 -2 0 2 4 6 8 10

Error in the estimated position (Toulouse's beltway)

Time of Day (hours:minutes)

m e te rs Up East North Fix mode

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Figure 6.22 Example of drifting position due to high multipath during a static period, between hour 9.35 and hour 9.40. Orange spots indicate estimated position and blue spots indicate reference trajectory.

Surprisingly the fix rate was slightly decreased from 47% to 40% on the beltway. However, it has greatly increased the reliability of ambiguity resolution as no wrong fixes were identified on the beltway as seen on Table 6.6.

6.3.2

Position Error on Data Set 2

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Figure 6.23 Position error in downtown Toulouse (data set 2). Black asterisk represents epochs when ambiguity vector is validated

and fixed as integer

Figure 6.24 Position error on Toulouse’s beltway (data set 2). Black asterisk represents epochs when ambiguity vector is validated and fixed as integer

Once again, the improvement is clear in both environments. The number of fixed ambiguity has been improved on the beltway while the wrong fix rate has decreased in both environments.

On the beltway epochs with fixed ambiguities are more “packed” together compared to the baseline solution. It is due to the fact that an ambiguity is maintained fixed as long as the cycle slip resolution module validates estimated integer cycle slips or has enough observations to estimate the cycle slip vector.

6.3.3

Conclusion on the Impact of the Cycle Slip Resolution Module

Performances improvements brought by cycle slip resolution module are summarized in Table 6.5.

Table 6.5 Performance summary of the baseline solution improved by GLONASS code inter-channel bias correction, the proposed observation weighting scheme and the cycle slip resolution module with the 2 studied data

sets

Horizontal Position Error

68th percentile 95th percentile 99th percentile _{Fix rate Wrong Fix rate}

Data Set 1 0.99 meters 3.21 meters 4.28 meters 15.6% 6.5%

urban 1.25 meters 3.65 meters 4.64 meters 2.5% 63.8%

Beltway 0.28 meters 0.67 meters 1.20 meters 39.9% 0.0%

Data Set 2 0.98 meters 2.91 meters 4.98 meters 12.9% 13.7%

urban 1.39 meters 3.34 meters 5.54 meters 2.6% 40.3%

beltway 0.48 meters 0.89 meters 1.15 meters 29.8% 9.8%

The continuous estimation of carrier phase ambiguities thanks to the cycle slip resolution module was shown to bring an improvement in both environments and both data sets, mostly in term of horizontal position error and ambiguity fixing rate. Continuous ambiguity estimation has a smoothing effect on the trajectory and improves horizontal error statistics, in all environments. Finally, continuous ambiguity resolution was found to increase ambiguity resolution reliability. However, in a high

12:30 12:45 13:00 13:15 13:30 -10 -8 -6 -4 -2 0 2 4 6 8 10

Error in the estimated position (Downtown Toulouse)

Time of Day (hours:minutes)

m e te rs Up East North Fix mode 13:40 13:50 14:00 14:10 14:20 -10 -8 -6 -4 -2 0 2 4 6 8 10

Error in the estimated position (Toulouse's beltway)

Time of Day (hours:minutes)

m e te rs Up East North Fix mode

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multipath environment, continuous ambiguity resolution tends to lead to a biased solution, particularly in a static environment where multipath effects on measurements don’t average out over time. Therefore, the addition of a multipath detection and exclusion algorithm, described in the next section, seems to be good answer to this issue.