ABSTRACT Bluetooth low energy (BLE)-based indoor localization has attracted increasing interests for its low-cost, low-power consumption, and ubiquitous availability in mobile devices. In this paper, a novel denoising autoencoder-based BLE indoor localization (DABIL) method is proposed to provide highperformance 3-D positioning in large indoor places. A deep learning model, called denoising autoencoder, is adopted to extract robust fingerprint patterns from received signal strength indicator measurements, and a fingerprint database is constructed with reference locations in 3-D space, rather than traditional 2-D plane. Field experiments show that 3-D space fingerprinting can effectively increase positioning accuracy, and DABIL performs the best in terms of both horizontal accuracy and vertical accuracy, comparing with a traditional fingerprinting method and a deep learning-based method. Moreover, it can achieve stable performance with incomplete beacon measurements due to unpredictable BLE beacon lost. INDEX TERMS Bluetooth low energy, indoor localization, fingerprinting, denoising autoencoder.
Xiao et al. REF proposed a novel denoising autoencoderbased BLE indoor localization method to provide highperformance 3D positioning in large indoor spaces.
3-D BLE Indoor Localization Based on Denoising Autoencoder
IEEE Access
IEEE Access
Computer Science