This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarget set is modeled as an independent and identically distributed (i.i.d.) cluster random finite set (RFS), and a consensus cardinality probability hypothesis density (CPHD) filter is run over the network to recursively compute in each node the posterior RFS density. Then a suitable cost function, expressing the discrepancy between the local posteriors in terms of averaged Kullback-Leibler divergence, is minimized with respect to the drift and orientation parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registraton and multitarget tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.

Distributed joint sensor registration and multitarget tracking via sensor network / Lin Gao, Giorgio Battistelli, Luigi Chisci, Ping Wei. - In: IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS. - ISSN 0018-9251. - STAMPA. - 56:(2020), pp. 1301-1317. [10.1109/TAES.2019.2929972]

Distributed joint sensor registration and multitarget tracking via sensor network

Lin Gao;Giorgio Battistelli;Luigi Chisci;
2020

Abstract

This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarget set is modeled as an independent and identically distributed (i.i.d.) cluster random finite set (RFS), and a consensus cardinality probability hypothesis density (CPHD) filter is run over the network to recursively compute in each node the posterior RFS density. Then a suitable cost function, expressing the discrepancy between the local posteriors in terms of averaged Kullback-Leibler divergence, is minimized with respect to the drift and orientation parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registraton and multitarget tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments on both tree networks and networks with cycles, as well as with both linear and nonlinear sensors.
2020
56
1301
1317
Goal 9: Industry, Innovation, and Infrastructure
Lin Gao, Giorgio Battistelli, Luigi Chisci, Ping Wei
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1161694
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