Naive Bayesian Classifier-based Collaborative Tracking and Recognition for Multi-radar Systems

This paper presents a naive Bayesian recognition and classification method based on extended Kalman filter, designed for multi-target tracking scenarios using multiple radars. The approach employs covariance intersection (CI) fusion to integrate data from diverse sources and obtains the target state...

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Bibliographic Details
Published inPhotonics & Electromagnetics Research Symposium (Online) pp. 1 - 9
Main Authors Ma, Shuoyang, Yuan, Ye, Yi, Wei
Format Conference Proceeding
LanguageEnglish
Published IEEE 21.04.2024
Subjects
Bayes methods
Feature extraction
Radar
Radar tracking
Robustness
Simulation
Target tracking
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ISSN2831-5804
DOI10.1109/PIERS62282.2024.10618205

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Summary:This paper presents a naive Bayesian recognition and classification method based on extended Kalman filter, designed for multi-target tracking scenarios using multiple radars. The approach employs covariance intersection (CI) fusion to integrate data from diverse sources and obtains the target state, accordingly. A naive Bayesian classifier is trained with various features extracted from the target state, enhancing the classification process. The numerical findings reveal the algorithm's robust performance in scenarios with multi-dimensional feature inputs, demonstrating its efficacy in classifying diverse target types.
ISSN:2831-5804
DOI:10.1109/PIERS62282.2024.10618205