## 4 Results and Discussion  
The result of a single test (a single SV crossing along a road section) was a dataset consisting of one series of reference positions, determined by the DR method, without taking into account measurement errors. The four series of positions estimated by the DR, EKF, GLSA, and GRA methods took measurement errors into account. In turn, 100 such datasets resulted from 100 tests (100 SV crossings along the same stretch of road). The sets of position series collected in this way were transformed into sets of distance series calculated between the reference position and the estimated position. After appropriate processing, these were used for comparative statistical analyses. The analyses were conducted to assess the accuracy of position coordinate estimation using the DR, EKF, GLSA, and GRA methods. Their results justified the necessity and provided the basis for developing a decision rule for selecting SV position coordinate estimation methods (among GRA and EKF methods) based on an accuracy criterion.  
First, tables with maximum values, arithmetic mean, and standard deviation of distances (Tables 1 and 2) were prepared based on them.  
In the second instance, they were used to prepare the distance and arithmetic mean distance graphs and the percentage, moving average, and moving RMS distances (Figures 6-23).  
Individual arithmetic mean values were calculated from 100 distance values corresponding to the exact moment \(k\).  
Moving average \(MA_{k}\) distance \(D_{tr}(k)\) to the reference position from the estimated position corresponding to the moment \(k\) was determined according to the formulae:* For a single passage SV \[MA_{k}=\frac{1}{10}\sum_{i=k-5}^{k+4}D_{re}(k=i),\] (13)
* For 100 SV crossings \[MA_{k}=\frac{1}{1000}\sum_{j=1}^{100}\sum_{i=k-5}^{k+4}\{D_{re}(k=i)\}_{j},\] (14) where: \(k=\{5,\;6,\;7,\ldots k_{last}-4\}\), \(j\)--SV crossing number. The moving root mean square \(MRMS_{k}\) distance \(D_{re}(k)\) to the reference position from the estimated position, corresponding to the moment \(k\), was determined according to the formula:
* For a single SV crossing \[MRMS_{k}=\sqrt{\frac{\sum_{i=k-5}^{k+4}\left(D_{re}(k=i)-\overline{D}_{re} \right)^{2}}{10}},\] (15)
* For 100 SV crossings \[MRMS_{k}=\sqrt{\frac{\sum_{j=1}^{100}\sum_{i=k-5}^{k+4}\left\{\left(D_{re}(k=i) -\overline{D}_{re}\right)^{2}\right\}_{j}}{1000}}.\] (16)  
The sample size is ten in the moving average and moving RMS calculations were performed according to relations (13)-(16). That is, each sample will contain one distance from the position estimated from simulated measurements, with gross error and nine distances from positions estimated from simulated measurements with mean error. This will ensure that the statistical analysis based on trend tracking will be meaningful, despite the more frequent occurrence of simulated measurements with mean error, and the less frequent occurrence of simulated measurements with gross error. In contrast, the use of moving RMS instead of its more commonly used moving standard deviation counterpart for statistical analysis is justified primarily by the small sample size. In statistical terms, moving RMS is the highest reliability estimator. 'Greatest reliability' does not refer to the results, but rather, to the random sample that will be most likely to be found with such a deviation in the population.