Assisted cold start method for GPS receiver with artificial neural network-based satellite orbit prediction

Abstract

Traditional GPS receivers usually take a long time to obtain their first position solution in the cold start mode. This paper presents a method for assisting the cold start of GPS receivers by autonomous satellite orbit prediction. In this method, the time-consuming broadcast ephemeris collection process in a traditional cold start is replaced by orbit prediction to obtain the satellite position information required for positioning. Thus, the time to first fix of the receiver can be reduced significantly. In addition, since the traditional orbit prediction algorithms based solely on physics-based models have limited accuracy, a hybrid model composed of dynamic models and artificial neural network models for orbit prediction is proposed to improve the accuracy of the orbit prediction. Taking GPS PRN1 as an example, the simulation results show that, in 75% of cases, the satellite position error after 7 days’ prediction with the dynamic models is less than 162.84 m, but this value can be reduced to 46.06 m with the proposed hybrid model; the improvement rate achieved is approximately 71.71%. Some positioning experiments with simulated satellite signals were conducted to assess the first-fix accuracy of a GPS receiver with the proposed satellite orbit prediction function. The results of this study offer insights for the design of advanced assisted GPS receivers.