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Wideband Multitarget Tracking Based on Dynamic Bayesian Network Learning in an Acoustic Sensor Array Network

delete2022-03-15
delete6
PRE
AI
W
Wenqiong Zhang
J
Jianfei Tong
M
Ming Bao *
X
Xiao–Ping Zhang *
X
Xiaodong Li
DOI:10.1109/JIOT.2021.3108528delete
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Abstract

Abstract

En 中文
The multitarget tracking (MTT) based on distributed fusion methods in an acoustic sensor array network (ASAN) is limited by the performance of measurement parameter estimators, such as the received signal strength (RSS) and the direction of arrival (DOA). For measurement parameters with low accuracy and resolution, the MTT may fail in subsequent steps, e.g., data association, because the loss of upstream information cannot be made up by downstream processing. Thus, we propose a new wideband MTT algorithm based on the dynamic Bayesian network (DBN), which treats the ASAN as an overall extended array, and directly estimates the target states from the raw acoustic data. The DBN fuses the near-field model, the acoustic propagation model. and the motion model. These submodels can be optimized by each other, improving the final estimation. Also, for each subband, target signals and the precision parameters of the sensor noise are treated as hidden random variables. Based on this, the weight of each subband can be automatically adjusted according to the accurate hidden variables. Besides, the optimization problem of the posterior probability is transformed into a graphical model learning problem. Moreover, for nonconjugate models, a novel algorithm based on Laplace approximations (LAs) with Newton's method (NM) is developed, i.e., DBN-LA-NM, bypassing data association. In addition, the corresponding Cramer-Rao lower bound and convergence conditions are derived. The numerical simulation results show that the proposed algorithm outperforms existing MTTs based on the near-field model in terms of accuracy, convergence, and computational complexity. Field experiments further verify the feasibility of the proposed algorithm.
Keywords:
Bayes methods
Target tracking
Internet of Things
Computational modeling
Sensor arrays
Heuristic algorithms
Acoustic sensors
Acoustic sensor array network (ASAN)
centralized fusion
direction of arrival (DOA)
dynamic Bayesian network (DBN)
multitarget tracking (MTT)
near-field target tracking
received signal strength (RSS)
wideband

Journal

IEEE Internet of Things Journal cover
IEEE Internet of Things Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W

Organization

U
university of chinese academy of sciences, cas
Scholars:
4.1W
Papers: 3.8W
Citations: 75
C
chinese academy of sciences
Scholars:
56.2W
Papers: 44.8W
Citations: 704