Indoor Wi-Fi Fingerprint Localization Based on SDAE and MLP with Self-attention Mechanism

For received signal strength (RSS) fluctuations, multipath and data redundancy issues based on Wi-Fi fingerprinting, this paper proposes an indoor localization system (SSMLoc), which combines stacked denoising autoencoders (SDAE) and multi-layer perceptron (MLP) with self-attention mechanism (SAM). The system uses SDAE to generate a deep robust representation of RSS data, which is subsequently used to build a localized fingerprint database. A simplified SAM reassigns various weights to the internal properties of deep Wi-Fi fingerprints, allowing the MLP network to better capture the critical features of Wi-Fi fingerprints. The performance of the SSMLoc system using the public dataset UJIIndoorLoc is validated in this paper, and the experimental findings reveal that the system has great robustness and enhances positioning accuracy and stability.