Summary of Hybrid Cnn-lstm Based Indoor Pedestrian Localization with Csi Fingerprint Maps, by Muhammad Emad-ud-din
Hybrid CNN-LSTM based Indoor Pedestrian Localization with CSI Fingerprint Maps
by Muhammad Emad-ud-din
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel Wi-Fi fingerprinting system uses Channel State Information (CSI) data for precise pedestrian localization. A hybrid architecture combines Convolutional Neural Network and Long Short-Term Memory Recurrent Neural Network models to generate a pedestrian trajectory hypothesis from CSI Fingerprint Map representations. A particle filter separates the most likely hypothesis matching human walk models. Compared to ConFi, DeepFi, and LSTM-based location classifiers, our method achieves marked improvement in moderately dynamic (RMSE: 0.36 m) and static environments (RMSE: 0.17 m). This proof-of-concept demonstrates reliable fine-grained Wi-Fi based pedestrian localization with sparse observations, limited infrastructure requirements, and moderate noise levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new system that helps find where people are on a map using Wi-Fi signals. It uses special information about the channels to create a map of what’s happening in different places. Then it uses two types of computers, called neural networks, to figure out where someone is walking. The results show that this system can be very accurate and could be used for things like tracking people or finding lost objects. |
Keywords
» Artificial intelligence » Lstm » Neural network » Tracking
