# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 5 Challenging Issues for Locata
### Raw Data Analysis of a LocataNet
Figure 8: The Received Signal Strength Indicator (rssi) change (dBm) with respect to distance. Figure 7: Probability of available measurements for each area for each LocataLites (LL). To assess the stochastic characteristics of the code pseudorange measurements, the time differences of the raw code pseudorange measurements from each LL are plotted in Figure 10, with different colours indicating the measurements in different areas. The distance from the LocataLite transmitter to the rover has been subtracted, therefore only the measurement errors are left and are shown in Figure 10. As expected, the magnitudes of the measurement errors for each LL are different in different areas. In general, errors for measurements in area 1 (blue) and 4 (magenta) are much smaller than those in areas 2 (red), 3 (cyan), and 5 (black). Frequent and significant measurement outliers are observed in each LocataLite, and the largest measurement errors can reach 30 metres. Figure 11 shows the corresponding positioning errors of using the raw code pseudorange measurements, with the left panels indicating the positioning errors in the east, north, and vertical component during the whole test period, and the right panels showing the corresponding positioning errors during the first 17 minutes when the vessel travelled within area 1. Left panels show that the positioning accuracy is higher in area 1, and it is worst in area 2. The right panels show more detailed results for area 1, indicating that the general position errors are within 2 metres, both in a horizontal and vertical sense, except for some epochs with large measurement outliers. The above analysis indicates that the Locata signal uncertainty is relatively high, so that precise positioning cannot be always guaranteed. Therefore, to improve the positioning accuracy, data preprocessing, as well as employing a robust positioning algorithm, is necessary. Similar to GNSS preprocessing, measurement outliers that contaminate raw measurements should be detected and excluded before being used for positioning. Figure 12 plots the time difference of the code pseudorange measurements after data preprocessing, which shows that the majority of measurement errors are within 5 m, and that errors in area 1 are obviously smaller than the errors in the other areas. Figure 13 shows the corresponding positioning errors of using data after preprocessing and then iteratively adjusting the measurement weights. When compared with the results in Figure 11, one can see that the positioning accuracy is significantly improved, especially in area 1, where the positioning errors are all less than 4 m, and 95% of them are less than 1 m.  
Figure 9: Trends of signal-to-noise ratio (dB) with respect to distance. Figure 11: Positioning errors using raw code pseudorange measurements: (**a**) whole experiment, (**b**) the first 17 minutes. Figure 12: Time differences of code pseudorange measurements from each LL with geometry distance corrections after data preprocessing (Unit: m). Figure 10: Time differences of raw code pseudorange measurements from each LL with geometry distance corrections (Unit: m).