Evaluation of C/N0 raw observation positioning by smartphone based on indoor asynchronous pseudolite

Abstract

AbstractWith the rapid development of the economy and information services, there is an increasing demand for indoor positioning. However, Global Navigation Satellite System (GNSS) signals face difficulties in penetrating buildings, making indoor positioning challenging. As a complementary solution to outdoor GNSS signals, pseudolites have shown promising results in various scenarios, and have become one of the research hotspots in the field of indoor positioning. Previous studies on indoor positioning with pseudolites have focused on issues such as initial position determination and carrier phase ambiguity resolution. However, most of these studies have remained in the research stage due to data post-processing and complex calculations. To overcome the limitations associated with the aforementioned factors, this paper proposes a real-time indoor positioning method that utilizes only a smartphone equipped with a built-in GNSS chip. By effectively obtaining pseudolite signal information, the smartphone establishes a simple model based on the distance and received power between the pseudolite and the smartphone. Kalman filtering is employed to mitigate the effects of multipath and occlusion. This simple and low-complexity model enables fast estimation of the user's distance from the pseudolite, thereby facilitating indoor positioning services. In this paper, the effectiveness and low complexity of the method are verified through data collection and experiments in the actual scene of the underground garage. The valuation results show that the root mean square error (RMSE) of its navigation and positioning is better than 3 m, which can meet the needs of daily life applications.