الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 208 |  |  |
| | | from | 208 | | |
Abstract
The reliability and robustness of positioning systems in urban and suburban environments are intrinsic. This is obvious fol- lowing the continuous increase of Intelligent Transportation Sys- tems (ITS) applications in such challenging environments. Global Navigation Satellite Systems (GNSS) represent the primary po- sitioning technique used for navigation purposes in these applica- tions, which can be satisfying in open-sky areas. However, GNSS cannot provide the same level of navigation performance in urban environments. One of the main reasons for this is the No-Line of Sight (NLOS) signals that reach the receivers through reflected paths. This contribution aims to develop new algorithms to de- tect the NLOS satellites and exclude the corresponding signals by employing two different strategies.
In the first strategy, the integration of GNSS and Light De- tection and Ranging (LiDAR) sensors is exploited, and a new algorithm is proposed for the detection of NLOS signals. GNSS Doppler observations are used to align the point clouds to the local frame. Moreover, rigorous accuracy analysis of the exist- ing error sources is applied in the algorithm. Real field data are
used to test and validate the proposed strategy and algorithm. Phase-smoothed code observations are employed to evaluate the accuracy improvement after excluding the NLOS observations. The results showed that the horizontal direction’s positional ac- curacy can be improved significantly after applying the proposed algorithm. This improvement reaches 10.40m with a mean value of 2.16m throughout the epochs with detected NLOS signals. Also, the Root Mean Square Error (RMSE) drops by 3.31m from 5.27m to 1.96m (63% improvement).
In the second approach, a new algorithm is proposed to de- tect and exclude the NLOS signals using 3D building models con- structed from Volunteered Geographic Information (VGI). Open- StreetMap (OSM) and Google Earth (GE) data are combined to build the 3D models incorporated with GNSS signals in the al- gorithm. Real field data are used for testing and validation of the presented algorithm and strategy. The accuracy improve- ment, after exclusion of the NLOS signals, is evaluated employ- ing phase-smoothed code observations. The results showed that applying the proposed algorithm can improve the horizontal posi- tioning accuracy remarkably. This improvement reaches 10.72m with a mean value of 0.80m over all epochs with detected NLOS signals. In addition, the RMSE drops by 1.64m from 3.57m to 1.93m (46% improvement).
In challenging areas, such as in suburban and urban environ- ments, it is difficult for GNSS to fulfil the positioning accuracy requirements. Consequently, it is intrinsic to have an indepen-
Abstract vi
dent positioning system capable of providing accurate and reli- able positional solutions over GNSS outages. This study exploits the integration of LiDAR, gyroscope, and odometer sensors, and a novel real-time algorithm is proposed for this integration. The algorithm adopts the line-based scan matching and applies condi- tions/constraints to adapt the technique to suburban and urban environments in ITS applications. In addition, Fault Detection and Exclusion (FDE) is performed to discover and eliminate the incorrectly matched lines between scans. Moreover, a “road seg- mentation” process is added to the algorithm to deal with the possible accumulation of errors and correct the heading of the moving vehicle. Real field data, collected by a moving vehicle, is used to test the presented algorithm. Three GNSS outages are introduced in the trajectory such that each outage lasts for five minutes. The results showed that using the proposed algorithm can achieve a promising navigation performance in urban envi- ronments. In addition, it is proved that the denser environments, that existed over the second and third outages, can provide better positioning accuracies as more features are extracted. The hori- zontal errors over the first outage, with less density of surround- ings, reached 7.7m (0.43%) with a mean value of 3.2m. Moreover, the horizontal errors in the denser environments over the second and third outages reached 5m (0.28%) and 4m (0.23%), with mean values of 2.3m and 1.9m, respectively.