Modelling and assessing reinforcement learning-assisted GNSS multipath estimation and mitigation for urban navigation

Abstract

Multipath has long been considered one of the major error sources for Global Navigation Satellite Systems (GNSS) in urban areas. In recent years, many multipath mitigation techniques based on offline machine learning have been proposed. However, collecting and labeling training data for offline learning based techniques is challenging for the multipath problem. On the other hand, it is difficult for the training data to completely capture multipath characteristics in various locations because of the high dependence of multipath on environments. This undoubtedly causes the performance of pre-trained models on real data to degrade. This paper proposes a multipath parameter estimation algorithm based on maximum likelihood estimation (MLE) using online reinforcement learning (RL). The proposed algorithm searches for optimal parameter estimates by interacting with the environment online, thus avoiding the problems associated with offline learning-based techniques. Specifically, the MLE of multipath parameters is formulated as an optimization problem, and a modified Q-learning-based algorithm is employed to solve the problem using an iterative method. Comprehensive performance evaluations of the proposed reinforcement learning-based multipath parameter estimation (RL-MPE) show that: 1) The proposed algorithm performs better in multipath parameter estimation compared to random search and the Bayesian optimization method named the Tree-structured Parzen Estimator (TPE). 2) RL-MPE has better performance for short multipath signals than the Multipath Estimating Delay Lock Loop (MEDLL) when using a comparable number of correlators. 3) RL-MPE achieves significantly better multipath mitigation performance on real urban data compared to the pre trained random forest (RF)-based multipath parameter estimator.