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
Recommended Citation
Guannan Liu, Prasanga Neupane, Hsiao Chun Wu, Weidong Xiang, Jinwei Ye, Limeng Pu, Shih Yu Chang, Yiyan Wu, and Kun Yan. "Novel Indoor Device-Free Human Tracking Using Learning Systems with Hidden Markov Models" IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB (2021). https://doi.org/10.1109/BMSB53066.2021.9547179
