A Bayesian Probabilistic TOA/AOA Hybrid Localization Algorithm in Multipath Environments

Cellular network-based positioning is a widely studied technology. It can be used to solve positioning problems when satellite signals cannot be received. TOA / AOA hybrid positioning is a reliable method in wireless positioning. However, due to multipath and NLOS propagation problems, the performance of the hybrid positioning algorithm is severely affected. Aiming at the above issues, we propose a TOA / AOA hybrid positioning algorithm based on the maximum posterior probability. By modeling the measurement error as a mixture Gaussian model, the algorithm accurately estimates the channel bias while calculating the user's position. Further, using the estimated channel information, the algorithm recalculates the user position to improve positioning accuracy. Compared with other positioning algorithms based on the Bayesian principle, the hybrid positioning method uses full of signal characteristics to estimate the transmitter position, so it can effectively reduce the impact of measurement errors. Simulation results show that the algorithm proposed in this paper effectively improves positioning accuracy.