Log-Regularized Dictionary-Learning-Based Reinforcement Learning Algorithm for GNSS Positioning Correction

In dynamic and complex environments, the positioning accuracy of global navigation satellite system (GNSS) will be seriously reduced. Deep reinforcement learning (DRL) has been found to give effective dynamic policy learning for complex GNSS positioning correction tasks. However, catastrophic interference in DRL models caused by the high correlation between successive positioning states, together with instability in gradient backpropagation in deep neural networks (DNNs), produces inaccurate DRL value approximation thereby degrades GNSS positioning performance. In this article, we develop a dictionary learning-based reinforcement learning (RL) algorithm with the nonconvex log regularizer for GNSS positioning correction. To avoid DNN instability problems, a dictionary learning-structured RL model is proposed. It has a feed-forward learning architecture obviating the need for gradient backpropagation. The nonconvex log regularizer for dictionary learning reduces the correlation between states and thereby alleviates interference in RL. This provides sparse representations, which can more effectively capture features and produce representations with lower biases than convex regularizers. Furthermore, the nonconvex optimization is made efficient through a decomposition scheme that generates an explicit closed-form solution using the proximal operator. Finally, based on the proposed dictionary learning-structured RL model, a novel positioning correction method is developed to enhance GNSS positioning accuracy. The experimental results indicate that the proposed method outperforms state-of-the-art sparse coding-based RL methods in benchmark environments. Moreover, the proposed method effectively improves GNSS positioning accuracy relative to the glsms Kalman filter acrlong KF method and the glsms weighted least squares acrlong WLS method.