Path Planning for Adaptive CSI Map Construction With A3C in Dynamic Environments

With the growing demand of Location-Based Service, the fingerprint localization based on Channel State Information (CSI) has become a vital positioning technology because it has easy implementation, low device cost and adequate accuracy which benefits from fine-grained information provided by CSI. However, the main drawback is that the approach has to construct the fingerprint map manually during the off-line stage, which is tedious and time-consuming. In this paper, we propose a novel data collection strategy for path planning based on reinforcement learning, namely Asynchronous Advantage Actor-Critic (A3C). Given the limited exploration step length, it needs to maximize the informative CSI data for reducing manual cost. We collect a small amount of real data in advance to predict the rewards of all sampling points by multivariate Gaussian process and mutual information. Then the optimization problem is transformed into a sequential decision process, which can exploit the informative path by A3C. We complete the proposed algorithm in two real-world dynamic environments and extensive experiments verify its performance. Compared to coverage path planning and several existing algorithms, our system not only can achieve similar indoor localization accuracy, but also reduce the CSI collection task.