Abstract
Satellite localization often suffers in terms of accuracy due to various reasons. One possible source of errors is represented by the lack of means to eliminate Non-Line-of-Sight satellite-related data. We propose here a method for fusing existing data with new information, extracted by using roof-mounted cameras and adequate image processing algorithms. The roof-mounted camera is used to robustly segment the sky regions. The localization approach can benefit from this new information as it offers a way of excluding the Non-Line-of-Sight satellites. The output of the camera module is a probability map. One can easily decide which satellites should not be used for localization, by manipulating this probability map. Our approach is validated by extensive tests, which demonstrate the improvement of the localization itself (Horizontal Positioning Error reduction) and a moderate degradation of Horizontal Protection Level due to the Dilution of Precision phenomenon, which appears as a consequence of the reduction of the satellites’ number used for localization.
Highlights
One of the most modern approaches in remote sensing is data fusion
Feedback loop and additional criteria for adaptively tuning the image processing The real signal tests show that the camera brings some improvements in the parameters regarding the threshold used onclearly the probability map
The contribution brought by the camera allowed to reduce the Horizontal Positioning Error (HPE) distribution, while only slightly increasing the Horizontal Protection Level (HPL), and very few areas remain where the camera degrades the positioning accuracy
Summary
One of the most modern approaches in remote sensing is data fusion. Global Navigation Satellite Systems (GNSS) receivers are embedded frequently into systems containing many other sensors. GNSS receivers are currently the standard equipment for precision positioning, navigation and timing. Some basic elements of GNSS are presented in Appendix A. In order to improve the performance of the positioning devices, manufacturers take advantage of the myriad of sensors available for implementing fusing approaches. Initiatives carried out at European Telecommunications Standards Institute (ETSI) level, especially illustrated in [1], demonstrate the natural trend towards an integrated concept that proposes a massive fusing approach between the present sensors.
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