Indoor Positioning for intelligent connected vehicles Using CGPC-EMPF

Positioning is one of the key technologies to support the application of intelligent connected vehicles (ICV). GNSS, as one of main measures of ICV positioning, cannot be available in indoor scenarios. The emergence of indoor wireless positioning technologies, such as UWB and WIFI, has solved this problem. However, they are affected by the non line of sight (NLoS) propagation of the signal, resulting in lower positioning accuracy. To improve the vehicle indoor positioning performance with NLoS state, we propose an ICV indoor positioning method called CGPC-EMPF. First, the Chan-GN algorithm is improved with permutation and combination consistency, whose result is used as the observation of particle filter. For the vehicle motion prediction without a Strapdown inertial navigation system, we propose a vehicle motion prediction model based on a priori information. Then, the NLoS correction model is established by a particle filter. Finally, the corrected measurements are substituted into the Chan-GN algorithm to obtain the final results. It realizes accuracy enhancement by introducing a vehicle motion state to correct errors coursed by NLoS. Compared with the multi-sensor fusion positioning algorithm, our method has lower complexity and cost due to a single sensor application.