Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning - Dongfang Mao; Haojie Lin; Xuyang Lou (Algorithms)
Accurate indoor positioning is essential for many applications. However, current methods often fall short in complex environments due to signal fluctuations. We propose a new indoor positioning approach, that is, improved sequential deep learning (ISDL), to address this issue. First, we apply sequential classification algorithms to progressively narrow the search space, reducing potential location regions into smaller neighborhoods. Next, we combine a deep neural network (DNN) with Weighted K-Nearest Neighbors (WKNN) to refine the final location prediction. Then, we validate our method using the publicly available <semantics>UJIndoorLoc</semantics> dataset, demonstrating superior accuracy compared to existing methods. Specifically, we achieved 95% floor prediction accuracy and reduced the average positioning error to just 7.82 m. By combining sequential classification and the DNN-WKNN hybrid model, we achieve better localization in complex indoor environments. This system offers practical improvements for real-time location-based services and other applications requiring precise indoor positioning.