Study of positioning estimation with user position affected by outlier: a case study of moving-horizon estimation filter

Many applications which require accurate location point positioning systems utilize global position system for pseudosciences. One of the main challenges faced by the system occurs due to the inherent errors that are a resultant of outliers. This considerably reduces the accuracy of the observations of global position system device. In this paper, we briefly introduce the problem of position estimation when the pseudo range measurements have an outlier. Moving horizon estimation algorithm has been adapted for the simulation result compared with the extended Kalman filter model, which is still imperfect for the case of outlier. The point at which a pseudo range becomes an outlier is considered at a fixed time instance. A simulation example is presented using an existing model with a moving horizon estimator and an extended Kalman filter. The moving horizon filter turns to be more robust than Kalman filtering with presence of outlier under certain choice of tunning parameter