CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach
Auburn University · Cisco Systems (United States)
Abstract
With the fast-growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted significant interest due to its high accuracy. In this paper, we present a novel deep-learning-based indoor fingerprinting system using channel state information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an offline training phase and an online localization phase. In the offline training phase, deep learning is utilized to train all the weights of a deep network as fingerprints. Moreover, a greedy learning algorithm is used to train the weights layer by layer to reduce complexity. In the online localization phase,…
Citation impact
- FWCI
- 65.83
- Percentile
- 100%
- References
- 56
Authors
4Topics & keywords
- Computer science
- Deep learning
- Artificial intelligence
- Channel state information
- Probabilistic logic
- Indoor positioning system
- Real-time computing
- Pattern recognition (psychology)