Distributed Joint Sensor Registration and Multitarget Tracking Via Sensor Network
For sensor networks with unknown relative positions and orientations, this work provides a distributed solution for joint registration and tracking, though it is incremental as it combines existing CPHD and consensus techniques.
This paper proposes a distributed method for joint sensor registration and multitarget tracking in sensor networks, using a consensus CPHD filter and minimizing averaged Kullback-Leibler divergence to estimate drift and orientation parameters. Simulations on tree and cyclic networks with linear and nonlinear sensors demonstrate the approach's effectiveness.
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, xpressing 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.