Abstract
WiFi fingerprinting has become a widely adopted solution for indoor localization due to its low deployment cost and wide availability. However, its positioning accuracy is often unsatisfactory, especially in complex environments involving multiple floors and buildings, where signal interference and structural complexity significantly degrade localization performance. To address these challenges, this paper proposes a deep neural network (DNN) framework that integrates a Stacked Autoencoder (SAE) for feature extraction and a multi-label classification strategy for accurate localization using received signal strength (RSS) data. To optimize the model parameters effectively in high-dimensional spaces, we introduce a Dual-Swarm Particle Swarm Optimization (DSPSO) algorithm. Unlike conventional PSO, which is prone to premature convergence, DSPSO partitions the particle population into two distinct sub-swarms with adaptive update mechanisms to enhance global exploration and avoid local optima. Experimental evaluations on seven 100-dimensional benchmark functions demonstrate that DSPSO outperforms traditional PSO and recent algorithms like the Sparrow Search Algorithm (SSA), achieving the global optimum in four cases. When applied to the WiFi localization task, the DSPSO-optimized SAE-DNN model achieves an average positioning error of 9.42 m-representing 13.74% improvement over the Support Vector Regression (SVR) model and 24.03% improvement over the non-optimized version. Furthermore, the model achieves 100% accuracy in building identification and 92.97% accuracy in floor prediction, proving its effectiveness in multi-building, multi-floor indoor localization scenarios.