indoor_position_fingerprint-master.zip_WSN Localization_indoor_i

# Indoor Positioning ### A simple fingerprinting-based localization project: - Setting up the beacons in a corridor of size 30m x 2.35m. - Creating the reference map by getting the RSS at predefined locations. Here, we took 20 measurements. - Developed an interpolating observation model. (k-NN and simple GP regression: https://www.robots.ox.ac.uk/~mebden/reports/GPtutorial.pdf) - Pair the observation model with a dynamic motion model (e.g. quasi-constant model) - Build a PF with the above models - Evaluate the results by using the model on the test data. ### Other details: - Beacons used were from kontact.io. - Product link: https://store.kontakt.io/our-products/27-beacon.html - Measurements app: Indoor Atlas proprietary app *SenseServe*. - Mobile platform: Android 6.0.1 - Mobile device: Nexus 6P - Not considering the wifi data as the time-stamp recorded is wrong. ### Beacon positions with measurements on the floor plan  ### Dynamic Model - So, we will be using the constant turn - constant velocity model with states being location(x coordinate and y coordinate), velocity (v), angular velocity (omega) ### Dynamic Model - It was decided to use the coordinated turn model.