gracecxj / indoor-localization

Indoor-Positioning

MSc Project: Indoor localization using neural networks

Brief introducton:

1. try to use clollected wifi fingerprint to train a rough neural network to roughly predict the location of a mobile device
2. using the accelerometer and the magnetometer data to enhence the time consistency of the location prediction(trajectory continuity)

Current progress:

• collected data insite, and get the ouput files from the prebuilt android mobile app(more information available: https://github.com/vradu10/LSR_DataCollection.git).
• preprocessed the data file, and converted them into standard inputs and outputs that the neural nets required.
• constructed 2 simple neural nets(classification, regression) to predict the location from wifi fingerprint
• implemented autoencoder layerwise to pretrain the neural nets(make use of the large amount of unlabeled wifi data collected previously)
• compare different network strcuctures([32,64,16] and [200,200,200]). Meantime, see how dropout layer and autoencoder pretrained weights helps the prediction process.
• get the transition probability matrix, and the median wr 'matrix'(each element in this two matrices indicate transition between two grid[start_grid -> row, end_grid -> column]).
• implement the Hidden Markov Model to enforce time consistency(2 adjacent timestep's location do not differ too much -> tragectory continuity).

Current results visualization:

The following plots is the "error in meters cdf" of different models. More detailed plots(such as error line plot, training curve plot .etc) can be found in results(*) directory. Note: C indicates classification models, while R indicates regression models.

simple vs dropout:

simple vs autoencoder:

autoencoder vs autoencoder+dropout:

To be continue:

• think about a way to integrate the accelerometer and magnetometer data to the inputs.
• collecting two dataset: dynamically and staticlly

Code Documentation:

The main part of this repository are three directories:

1. "Data" directory

• "background" directory : the original data collected by the mobile app
• "masking area" directory : the two types of masking areas (forground)
• "unlabelled" directory : the preprocessed unlabelled WiFi data (no location)

2. "Source code" directory

• "preprocessing" directory
  • WifiPreprocessing.py : scan all the wifi signal in background file, write distinct access point into a file, it can iterate over all the background files in a specified directory
  • Masking.py : the masking function defined in this file can take a list of positions as inputs, which construct a polygon area. index the whole world rectangular space(60*80), and then return the grid index which are inside the polygon.
  • unlabelled.py : traverse all background files and get the unlabelled wifi data (used only for training autoencoders)
• "utilities" directory
  • Plotting.py : some functions related to plot the training curve and the cdf
  • sdae.py : constructing a layer-wise training process of autoencoders
  • SensorParse.py : Pre-processing the background files, generate standard input data, and instantiate a SensorFile object for each background file collected
  • SensorParse2.py : some changes for hmm over the previous code
• "core" directory
  • main.py : training the models (classification and regression)
  • main_sdae.py : training the models (classification and regression) that are pretrained by autoencoders
  • main_hmm.py : training the models (classification and regression) that refined by HMM

3. "Experiement results" directory

• some output cdf plots and error line plots of different models