A resampling strategy based on bootstrap to reduce the effect of large blunders in GPS absolute positioning

In the absence of obstacles, a gps device is generally able to provide continuous and accurate estimates of position, while in urban scenarios buildings can generate multipath and echo-only phenomena that severely affect the continuity and the accuracy of the provided estimates. receiver autonomous integrity monitoring (raim) techniques are able to reduce the negative consequences of large blunders in urban scenarios, but require both a good redundancy and a low contamination to be effective. in this paper a resampling strategy based on bootstrap is proposed as an alternative to raim, in order to estimate accurately position in case of low redundancy and multiple blunders: starting with the pseudorange measurement model, at each epoch the available measurements are bootstrapped—that is random sampled with replacement—and the generated a posteriori empirical distribution is exploited to derive the final position. compared to standard bootstrap, in this paper the sampling probabilities are not uniform, but vary according to an indicator of the measurement quality. the proposed method has been compared with two different raim techniques on a data set collected in critical conditions, resulting in a clear improvement on all considered figures of merit.