An Integrated Urban Positioning Algorithm Using Matching, Particle Swam Optimized Adaptive Neuro Fuzzy Inference System and a Spatial City Model.

In urban environments, Global Positioning System (GPS) signals are often reflected or blocked by buildings causing multipath effects and Non-Line-Of-Sight (NLOS) reception. These effects degrade GPS positioning performance. Although improved receiver and antenna design can reduce the multipath effect to some extent, NLOS elimination is limited due to sensor cost. Measurements based modelling methods have shown promise to reduce NLOS, while positioning accuracy is limited by the correct classification of the signal reception types. These limitations are addressed in this paper by developing an integrated algorithm using matching, Particle Swam Optimized Adaptive Neuro Fuzzy Inference System (PSO-ANFIS) and a spatial city model. The user's location is determined by matching the high accuracy signal reception type determined by PSO-ANFIS with satellite visibility of each candidate position determined by the ray-tracing and a 3D city model. The results from a field test in an urban area in Taiwan show that the PSO-ANFIS based algorithm achieves a classification accuracy of 96%, 88%, and 91% for LOS, multipath and NLOS signals, respectively, which is superior to the other classification algorithms. The final horizontal positioning accuracies in terms of the Root Mean Square Error (RMSE) are 3.20 m using a commercial receiver and 1.88 m using a geodetic receiver. The proposed algorithm has consistent improvements of 88% and 70% respectively compared to the traditional Weighted Least Square (WLS) based positioning method, and is the best compared to the other state-of-the art methods with the improvements ranging from 15% to 77% depending on the test conditions. [ABSTRACT FROM AUTHOR]