Abstract:
This article proposes recurrent neural networks (RNNs) for the WiFi fingerprinting indoor localization. Instead of locating a mobile user's position one at a time as in t...Show MoreMetadata
Abstract:
This article proposes recurrent neural networks (RNNs) for the WiFi fingerprinting indoor localization. Instead of locating a mobile user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at the trajectory positioning and takes into account the correlation among the received signal strength indicator (RSSI) measurements in a trajectory. To enhance the accuracy among the temporal fluctuations of RSSI, a weighted average filter is proposed for both input RSSI data and sequential output locations. The results using different types of RNN, including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional RNN (BiRNN), bidirectional LSTM (BiLSTM), and bidirectional GRU (BiGRU) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of 0.75 m with 80% of the errors under one meter, which outperforms K-nearest neighbors algorithms and probabilistic algorithms by approximately 30% under the same test environment.
Published in: IEEE Internet of Things Journal ( Volume: 6, Issue: 6, December 2019)
Funding Agency:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Localization Accuracy ,
- Recurrent Neural Network ,
- Indoor Localization ,
- Received Signal Strength Indicator ,
- Fingerprint ,
- Short-term Memory ,
- Average Error ,
- Long Short-term Memory ,
- Localization Error ,
- Gated Recurrent Unit ,
- Bidirectional Long Short-term Memory ,
- Received Signal Strength ,
- Bidirectional Recurrent Neural Network ,
- Average Localization Error ,
- Time Step ,
- Social Distancing ,
- Hidden Layer ,
- Previous Step ,
- Training Phase ,
- Multiple-input Single-output ,
- Recurrent Neural Network Model ,
- Training Trajectories ,
- Random Trajectories ,
- Multiple Outputs ,
- Probabilistic Method ,
- Kalman Filter ,
- Consecutive Locations ,
- Multilayer Perceptron ,
- Feed-forward Network
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Localization Accuracy ,
- Recurrent Neural Network ,
- Indoor Localization ,
- Received Signal Strength Indicator ,
- Fingerprint ,
- Short-term Memory ,
- Average Error ,
- Long Short-term Memory ,
- Localization Error ,
- Gated Recurrent Unit ,
- Bidirectional Long Short-term Memory ,
- Received Signal Strength ,
- Bidirectional Recurrent Neural Network ,
- Average Localization Error ,
- Time Step ,
- Social Distancing ,
- Hidden Layer ,
- Previous Step ,
- Training Phase ,
- Multiple-input Single-output ,
- Recurrent Neural Network Model ,
- Training Trajectories ,
- Random Trajectories ,
- Multiple Outputs ,
- Probabilistic Method ,
- Kalman Filter ,
- Consecutive Locations ,
- Multilayer Perceptron ,
- Feed-forward Network
- Author Keywords