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.

Distributed joint detection, tracking, and classification via labeled multi-Bernoulli filtering / Gaiyou Li, Giorgio Battistelli, Luigi Chisci, Lin Gao, Ping Wei. - In: IEEE TRANSACTIONS ON CYBERNETICS. - ISSN 2168-2267. - STAMPA. - (2022), pp. 1-13. [10.1109/TCYB.2022.3208038]

Distributed joint detection, tracking, and classification via labeled multi-Bernoulli filtering

Giorgio Battistelli;Luigi Chisci;Lin Gao;
2022

Abstract

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.
2022
1
13
Gaiyou Li, Giorgio Battistelli, Luigi Chisci, Lin Gao, Ping Wei
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1283383
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