Trilateration based on RSSI values in transmitters and receivers
⭐ When using this resource, please cite the original publication:
- J. Landívar, C. Ormaza, V. Asanza, V. Ojeda, J. C. Avilés and D. H. Peluffo-Ordóñez, "Trilateration-based Indoor Location using Supervised Learning Algorithms," 2022 International Conference on Applied Electronics (AE), 2022, pp. 1-6, doi: 10.1109/AE54730.2022.9920073.
This dataset has received power measurements from three receivers using a mobile transmitter. An area of 4m x 2m was divided into 200 squares. The transmitter was positioned in each grid and 100 measurements of RSSI values received at three fixed receivers were made, as shown in the graph. This dataset has a total of 2000 rows corresponding to 100 examples in each of the 200 grid positions. In addition, the dataset has the following columns:
- Grid: which has values from 1 to 200.
- Position in X: with values from 0 to 4 meters.
- Position in Y: with values from 0 to 2 meters.
- RSSI in receiver 1
- RSSI in receiver 2
- RSSI in receiver 3
- To perform measurements over larger areas
- Increase the number of receivers to improve prediction accuracy
- Use more than one transmitter, to track more objects
The attached document presents the script with which the data is preprocessed, characterized and classified. All this code was developed in matlab.
All source code is available in the following github repository: https://github.com/vasanza/WiFi_RSSI_Localization