Novel Indoor Device-Free Human Tracking Using Learning Systems with Hidden Markov Models

Publication Date

8-4-2021

Document Type

Conference Proceeding

Publication Title

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB

Volume

2021-August

DOI

10.1109/BMSB53066.2021.9547179

Abstract

This paper proposes a novel indoor device-free localization and tracking approach using the received signal-strength indicators (RSSIs) of WiFi signals. The RSSI feature-vectors simulated by a channel-propagation emulator software are adopted as the training data for our proposed scheme. Prevalent discriminative machine-learning methods are used to predict the locations of a moving human-object. Hidden Markov models (HMMs) are also incorporated with such machine-learning techniques for robust and reliable indoor tracking. In this work, we partition the given indoor geometry into several equi-sized zones and then convert the underlying localization/tracking problem to the classical multi-classification problem. Simulation results demonstrate that the gradient boosting decision-tree (GBDT) classifier in conjunction with the Viterbi algorithm over hidden Markov models leads to the highest localization-accuracies of 83.9% for eight zones and 71.4% for sixteen zones. As a result, our proposed new indoor localization and tracking scheme can be very promising for many indoor device-free surveillance applications in the future.

Funding Number

LEQSF(2021-22)-RD-A-34

Keywords

hidden Markov models, Indoor dynamic tracking, machine learning, received signal strength indicator (RSSI)

Department

Applied Data Science

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