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The development of integrated applications will inevitably continue to grow, and become more widely used, especially when the European Galileo GNSS is fully operational. The European satellite navigation system Galileo is expected to be in full operation by 2010. It will represent a signi cant addition and improvement to GPS for all applications and systems that currently integrate GPS and GIS.

We get such an equation for each satellite in view. In general, for m satellites, we can write this system of m equations in matrix form: P 1 x P 2 x = P 3 x M P m x P 1 y P 2 y P 3 y M P m y P 1 z P 2 z P 3 z M P m z P 1 t P 2 x v1 t y v 2 P 3 + 3 z v t t M M m P t

The Gallup Poll Panel is a consumer panel that asks members to participate in a variety of opinion, social and market research surveys. Attrition is a serious concern for consumer panels as it can introduce nonresponse bias and it can introduce extra recruitment costs. The analysis presented here suggests that the demographic characteristics associated with attrition in the GPP are similar to the characteristics associated with nonresponse in many RDD surveys. Males, younger adults, those with lower education and those of a race other than White drop out at higher rates than others. In addition, length of membership and number of survey requests have an impact on attrition, with a median survival time of 11 months. The effects of three experiments on attrition were also studied. The experiments consisted of a short versus long telephone recruit introduction, a request for signature on the intake mail questionnaire and receipt of only self-administered questionnaires or of one telephone interview per month. Only the last of these experiments produced a significant effect on attrition in both the bivariate analysis and the logistic regression. Attrition was found to reduce when the first survey each month was carried out by telephone. Finally, the mix of surveys in which panel members took part appears to have an impact on attrition. Panel members who took part in more social and political surveys, as opposed to market research surveys, were less likely to drop out of the panel.

However, height errors provided by RAW+HA are still large (15 m 20 m), because of the inaccurate raw GPS positions. Table 7.4 shows the calculated position error. In the relatively rugged terrain where the data were collected, i.e. the hills of South Wales, any error in the computed receiver horizontal position may result in a large interpolated height error. This is because hill slope in the vicinity of these road surfaces may be 45 degrees or higher. This slope can give a very rapid change of height any distance away from the road. Even if the height aiding is used alone (i.e. RAW+HA), all the interpolated heights are still obtained from the road. RRF will always try to snap the raw position onto the road centre-lines to obtain a more accurate interpolated height. As shown in Table 7.4, there is no signi cant difference (less than 0.01 m) between the mean position errors of the three interpolation algorithms when using the same resolution DTM. The larger resolution 10 m DTM provides slightly more accurate positions (less than 0.14 m) than the 50 m DTM in terms of mean positional error. In addition, it is clear (see Figure 7.4) that height aiding does improve the positioning accuracy of a single receiver (i.e. about 1 m improvement in horizontal accuracy). In order to test the overall performance of RRF, both OS road centre-line data and OS DTM data are used to compute an RRF position (Table 7.5). Here, the height error is reported as a general Root Mean Square Error (RMSE), which is given by:

and rn is the distance to the nth target, namely, rn x2 yn y 2 2 n The desired image to be reconstructed can be represented as u x; y X