DNN-Based 3D Relative Positioning Using Inter-Node Distances in Indoor Environments

Abstract

In indoor environments, the accuracy of GPSbased positioning is significantly degraded by signal blockage, multipath propagation, and structural interference, making alternative approaches essential for precise indoor localization. This paper presents a deep neural network (DNN)-based threedimensional relative positioning method that relies solely on internode distance information. Experimental results show that the DNN-based method maintains more stable MAE performance as distance measurement errors increase and consistently achieves lower root mean squared error (RMSE) than the grid-based algorithm across all node configurations. In particular, as the distance measurement error grows, the grid-based algorithm exhibits pronounced performance degradation due to error accumulation during the grid-search process, whereas the DNN-based method shows more gradual degradation by learning the nonlinear relationship between distance information and relative coordinates. Furthermore, the results indicate improved scalability and robustness to distance measurement noise as the number of nodes increases. These findings suggest that the DNN-based approach has potential as an effective three-dimensional relative positioning solution for indoor node systems such as drone swarms and mobile robot networks.