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GNSS Solutions : How important is GNSS observation weighting ?
| Content Provider | Semantic Scholar |
|---|---|
| Author | Petovello, Mark G. |
| Abstract | How BLOCKINimportant BLOCKINis GNSS BLOCKINobservation weighting? I n the early days of GPS, most receivers tracked only as many satellites as were required to compute a position. This meant that observation weighting was not needed and not even possible when processing on the epoch-by-epoch level. Soon, though, receivers were capable of tracking all satellites " in view, " and instead of the four minimum pseu-dorange observations required for a three-dimensional position, five, six, or more pseudoranges could be available at each epoch. GNSS observation redundancy will increase further as GLONASS and Galileo approach their full constellations. Inevitably, redundant observations are inconsistent. At first sight, this might seem a nuisance best avoided by selecting a suitable non-redundant subset of the observations to compute position and receiver clock bias — for example, the one yielding minimum GDOP. In reality, however, GDOP tells nothing about the actual errors of the observations, and the chosen subset may produce a larger position error than other subsets would. It is far better to exploit the inconsistencies using statistical methods such as least-squares (LS) estimation, Kal-man filtering, and hypothesis testing. This increases the positioning precision , allows checking for failures, and reduces the probability of undetected gross errors. However, exploiting the inconsistencies requires that the relative precision of each observation with respect to the other observations be known. A precise observation should have a higher weight and thus contribute more to the computed parameters than an imprecise one. Proper observation weighting is only possible if the variance -covariance matrix (VCM) of the observations is known, and in fact LS estimation and Kalman filtering yield the most precise results only if the correct VCM is used (advanced approaches with less stringent requirements are beyond the scope of this column). Knowledge of the VCM is even more important in view of statistical failure detection and identification; inappropriate weights may cause outli-ers to remain undetected and truly accurate observations to be rejected, thus inverting the desired benefit of quality control into a considerable loss of accuracy. Both redundant observations and proper observation weighting are essential for obtaining a precise and reliable estimate. Proper observation weighting, as it turns out, is not a trivial task with GNSS observations. The reason is that the variance must incorporate all unmodeled effects and thus depends on factors such as tracking loop characteristics , receiver and antenna hardware , signal strength, receiver dynamics , multipath … |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://insidegnss.com/wp-content/uploads/2018/01/JanFeb07GNSSSolutions%20(secured).pdf |
| Alternate Webpage(s) | http://www.insidegnss.com/auto/JanFeb07GNSSSolutions%20(secured).pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
