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Cooperative Localization and Target Tracking With Data Exception Based on Classification
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AI
DOI：10.1109/JSEN.2024.3485069.png)
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摘要
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This article considers the problem of cooperative localization and target tracking (CLTT) with data exception from compromised/faulty sensors. An efficient classification approach is used to resist abnormal data from neighbors and then improve fusion performance. Unlike traditional fault-diagnosis-based methods, the proposed classification-based CLTT can be deployed and executed online without condition monitoring, achieving high real-time capability and reliability. To validate the effectiveness of the proposed algorithms, simulation examples and real-world experiments are provided.
Keyword:
Classification
cooperative localization and target tracking (CLTT)
data exception
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
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引用论文
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INFORMATION FUSION
OAAI
INFORMATION FUSIONIF15.5
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OAAI
IEEE SENSORS JOURNALIF4.5
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