Improving Position Estimates from a Stationary GNSS Receiver using Wavelets and Clustering

Many positioning applications utilize global navigation satellite systems (GNSS) derived position estimates for stationary positions. Inexpensive navigation-grade receivers provide estimates within a few meters in relatively open skies, while more specialized devices, typically distinguished by specialized antenna design and additional post processing can achieve sub-meter accuracy. These latter devices can be two orders of magnitude more expensive than navigation-grade receivers but are still subject to measurement error due to severe multipath in built-up areas. In our experiments we post-process positions computed by an inexpensive receiver by applying wavelet filtering following by clustering and characterization. This produces a reliable and significant reduction in variance of the estimate, a normalization of the data scatter-distribution and a characterization of the estimate that is amenable to a wider range of statistical comparisons and tests than would be possible for unfiltered, highly non-Gaussian distributions, especially as occur in urban canyon circumstances