TY - JOUR
T1 - Deep Learning Model for Robust Target Tracking Using TDoA Probabilistic Image
AU - Lee, Sungho
AU - Shim, Jaewoong
N1 - Publisher Copyright:
© 2023, Korean Institute of Communications and Information Sciences. All rights reserved.
PY - 2023/7
Y1 - 2023/7
N2 - Ultra Wide Band (UWB) based indoor positioning methods utilizing Time Difference of Arrival (TDoA) are commonly used; however, their performance significantly degrades in environments with high Additive White Gaussian Noise (AWGN). Although many studies have attempted to remove AWGN from TDoA, these approaches require additional positioning methods and lack robustness in various environments. In this paper, we propose a 'TDoA Probabilistic Image Based Target Tracking (TPITT)' method that robustly estimates an object's position using TDoA with AWGN. TPITT generates probability images of object presence in each region using TDoA and employs a 'Convolution-LSTM' model to estimate object coordinates. Experiments demonstrate the proposed method's robustness and low prediction error across diverse environments. Notably, TPITT is more effective than the prior study 'TDoA Image Based Target Tracking (TITT)' in environments with high AWGN.
AB - Ultra Wide Band (UWB) based indoor positioning methods utilizing Time Difference of Arrival (TDoA) are commonly used; however, their performance significantly degrades in environments with high Additive White Gaussian Noise (AWGN). Although many studies have attempted to remove AWGN from TDoA, these approaches require additional positioning methods and lack robustness in various environments. In this paper, we propose a 'TDoA Probabilistic Image Based Target Tracking (TPITT)' method that robustly estimates an object's position using TDoA with AWGN. TPITT generates probability images of object presence in each region using TDoA and employs a 'Convolution-LSTM' model to estimate object coordinates. Experiments demonstrate the proposed method's robustness and low prediction error across diverse environments. Notably, TPITT is more effective than the prior study 'TDoA Image Based Target Tracking (TITT)' in environments with high AWGN.
KW - Deep learning
KW - Indoor tracking
KW - Real time localization
KW - TDoA
KW - UWB communication systems
UR - http://www.scopus.com/inward/record.url?scp=85189145270&partnerID=8YFLogxK
U2 - 10.7840/kics.2023.48.7.807
DO - 10.7840/kics.2023.48.7.807
M3 - Article
AN - SCOPUS:85189145270
SN - 1226-4717
VL - 48
SP - 807
EP - 815
JO - Journal of Korean Institute of Communications and Information Sciences
JF - Journal of Korean Institute of Communications and Information Sciences
IS - 7
ER -