Distributed Joint Detection, Tracking, and Classification via Labeled Multi-Bernoulli Filtering

• Published in: IEEE transactions on cybernetics Vol. 54; no. 3; pp. 1429 - 1441
• Main Authors: Li, Gaiyou, Battistelli, Giorgio, Chisci, Luigi, Gao, Lin, Wei, Ping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Data integration; Distributed multiobject fusion; Filtration; generalized covariance intersection (GCI); Kinematics; labeled multi-Bernoulli (LMB); labeled random finite sets (RFS); minimum information loss (MIL); Multisensor fusion; Peer-to-peer computing; Probability density function; Radio frequency; Sensors; Target tracking; Tracking; Trajectory
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2022.3208038

In this article, we propose a novel approach to distributed joint detection, tracking, and classification (D-JDTC) of multiple targets by means of a multisensor network. The proposed approach relies on labeled multi-Bernoulli (LMB) random finite set modeling of the multisensor state, and consists of two main tasks, that is, local filtering in each individual node and data fusion among multiple nodes. For local filtering, the LMB filter is extended to JDTC by augmenting the target state to incorporate class and mode information. Further, the well-known generalized covariance intersection and recently developed minimum information loss fusion paradigms are exploited for data fusion among sensors. The effectiveness of the resulting algorithm, called D-JDTC-LMB, is assessed via simulation experiments.