Multitarget Tracking Based on Dynamic Bayesian Network With Reparameterized Approximate Variational Inference
Autor: | Xiao-Ping Zhang, Wenqiong Zhang, Ming Bao, Xiaodong Li, Jun Zhang |
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Rok vydání: | 2022 |
Předmět: |
Optimization problem
Computer Networks and Communications Computer science Inference Computer Science Applications Hardware and Architecture Joint probability distribution Signal Processing Convergence (routing) Graphical model Assignment problem Random variable Algorithm Dynamic Bayesian network Information Systems |
Zdroj: | IEEE Internet of Things Journal. 9:11542-11559 |
ISSN: | 2372-2541 |
DOI: | 10.1109/jiot.2021.3131308 |
Popis: | Multi-target tracking (MTT) is an important component of situation-awareness based on the Internet of Things (IoT). Existing algorithms mainly focus on tracking based on conventional measurements, e.g., bearings or ranges. However, measurement parameter estimations are considered in isolation, limiting the accuracy and resolution of MTT, and the related data association is an NP-hard multi-dimensional assignment problem. In this paper, we develop a new one-step MTT algorithm based on a novel dynamic Bayesian network (DBN), i.e, DBNMTT. The new MTT algorithm directly infers target states from the raw measurement data by fusing the array signal model, the signal propagation model, and the motion model. In this new DBNMTT framework, we treat target states and conventional measurements such as bearings and target energies as hidden random variables. The posterior joint probability optimization problem is translated into the problem of graphical model learning. In this way, we can improve the accuracy and resolution of MTT and convert the NP-hard data association problem to a hidden variable learning problem. For non-conjugate models in the DBNMTT, we develop a novel reparameterized approximation variational inference (ReAVI) approach to solve the learning problem. The ReAVI converts non-conjugate models to conjugate models with new parameters and reuses the mean-field algorithm. The performance of our proposed new MTT method, namely DBNMTT-ReAVI, is analyzed on extensive simulations in challenging scenarios. The simulation results show that the DBNMTT-ReAVI algorithm is superior to conventional measurement based MTT algorithms in several aspects including the success probability, the convergence, the resolution, and the accuracy. |
Databáze: | OpenAIRE |
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