This blog contains the papers I have read for my summer research: Applying machine learning to indoor fingerprinting positioning, especially using deep learning.

开篇入门(chinese version):

Please skip this part if you don’t speak chinese.

Overview of indoor positioning

  1. A Survey of Indoor Localization Systems and Technologies
  2. Overview of indoor positioning system technologies
  3. Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Dataset used

Crowdsourced WiFi database and benchmark software for indoor positioning

Long-Trem WiFi Fingerprinting Dataset for Reserach on Robust Indoor Poitioning


deep learning

  1. Low-effort place recognition with WiFi fingerprints using deep learning

    This article use deep neural networks and Autoencoders to do the floor Classification, but no positioning prediction. I have done the positioning part, get a good result.
    code of this paper: github

  2. A Deep Learning Approach to FingerprintingIndoor Localization Solutions

    It use two methods to solve the small dataset problem. one is using data augmentation. The sequence of the APs will change(very doubt at this method) ; Anther method is to use transfer learning, only similar dataset can help.

  3. Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation Using Wi-Fi Fingerprinting Based on Deep Neural Networks

    get more advance based on paper 1. Not only floor detection, but also positioning estimation.

  4. Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

    This is an overview. Two keywords: fingerprint, WiFi

  5. Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization

    basically the same as paper 2.

  1. CNN based Indoor Localization using RSS Time-Series

    using CNN to deal with long-term(time—series) dataset

  2. todo


  1. Clustering benefits in mobile-centric WiFi positioning in multi-floor buildings

    cluster method for our dataset1

  2. 基于K均值聚类算法的位置指纹定位技术(chinese version)


  3. Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems*

    The UJI kNN algorithm for dataset.
    This article mainly concentrates on the Wi-Fi Indoor positioning systems based on fingerprinting and k-NN. It mentions Non-heard data processing, data preprocessing, and all kinds of distance calculating.

  1. Adaptive K-nearest neighbour algorithm for WiFi fingerprint positioning

    This article focus on how to improve K nearest neighbour algorithm

Kriging algorithm:

  1. Method for yielding a database of locationfingerprints in WLAN

    using kriging to generate fingerprint data

  2. Fingerprint Space Building Algorithm with Kriging for Large Positioning Regional Environment

  3. Applying Kriging Interpolation for WiFiFingerprinting based Indoor Positioning Systems

  4. kriging tutoril(Chinese version)

other algorithms need arrange

  1. Dealing with Insufficient Location Fingerprints in Wi-Fi Based Indoor Location Fingerprinting

other maybe useful articles or resources: