Novel Deep-Learning-Aided Multimodal Target Tracking

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Existing interacting multiple models (IMMs) are limited by the time delay in responding to system model jumps due to the nature of the soft hand-off algorithm that interacts among subfilters. To address this issue, a novel method for deep-learning-aided localization of a multimodel system is proposed in this paper. The main contribution of the proposed algorithm is that a mode estimation network based on a bidirectional long short-term memory network (BiLSTM) is newly proposed to quickly and accurately estimate the multimodal system mode, which minimizes the delay. In addition, a federated Kalman filter with a selective reinitialization algorithm from the proposed BiLSTM is proposed for better estimation of multimodal systems. Simulation and flight test results of a UAV demonstrate that the proposed algorithm yields better localization performance than the conventional IMM algorithm because the proposed mode estimation network has fast and accurate mode detection.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-09
Language
English
Article Type
Article
Citation

IEEE SENSORS JOURNAL, v.21, no.18, pp.20730 - 20739

ISSN
1530-437X
DOI
10.1109/JSEN.2021.3100588
URI
http://hdl.handle.net/10203/288477
Appears in Collection
AE-Journal Papers(저널논문)
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