A comparative performance analysis of position estimation algorithms for GNSS localization in urban areas

Global Navigation Satellite Systems (GNSS) have become an integral part of all applications where mobility plays an important role. However, The performances of GNSS-based positioning systems can be affected in constrained environments (urban and indoor environments), due to masking of satellites by buildings and multipath effects. In this paper, a comparative investigation on classical GNSS localization algorithms in urban areas is presented and analyzed in terms of mean squared error. As a result, Kalman filter estimation shows the best error performance in good environments (all satellites are in direct sight). Nevertheless, in constrained environments, the Kalman filter and least square method show important positioning errors because their measurement noise model is unsuitable.