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Recurrent_Neural_Networks_for_Accurate_RSSI_Indoor_Localization
Brosnan/Recurrent_Neural_Networks_for_Accurate_RSSI_Indoor_Localization
Recurrent_Neural_Networks_for_Accurate_RSSI_Indoor_Localization is a machine learning model from Brosnan. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for keras. The card lists the license as cc-by-nc-sa-4.0.
Source code for M.T. Hoang, B. Yuen, X. Dong, T. Lu, R. Westendorp and K. Reddy, “Recurrent Neural Networks for Accurate RSSI Indoor Localization,” IEEE Internet of Things Journal, 2019
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Updated Dec 2, 2022
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4.3 MB
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At a glance
- Library
- keras
- License
- cc-by-nc-sa-4.0
- Access
- Public
- Created
- Dec 2, 2022
- Updated
- Dec 2, 2022
- SHA
- 68a20d43
Model card
From the Hugging Face model README
Recurrent Neural Networks for Accurate RSSI Indoor Localization
Source code for M.T. Hoang, B. Yuen, X. Dong, T. Lu, R. Westendorp and K. Reddy, “Recurrent Neural Networks for Accurate RSSI Indoor Localization,” IEEE Internet of Things Journal, 2019
Folder Structure
- Step1_FilterDatabase.m: Filter the database with Average Weighted Filter or Mean Filter
- Step2_Create_RandomTraj.m: Generate random training trajectories under the constraints that the distance between consecutive locations is bounded by the maximum distance a user can travel within the sample interval in practical scenarios.
- Step2_CreateInputTraining_Model5: Create the input training data for P-MIMO LSTM
- RNN models training code (Using Keras and Tensorflow)
- LSTM_Model_1.py
- LSTM_Model_2.py
- LSTM_Model_3.py
- LSTM_Model_4.py
- LSTM_Model_5.py
- Library
- keras
- License
- cc-by-nc-sa-4.0
- Created
- Dec 2, 2022
- Updated
- Dec 2, 2022