2 research outputs found
Consensus-based joint target tracking and sensor localization
In this paper, consensus-based Kalman filtering is extended to deal with the
problem of joint target tracking and sensor self-localization in a distributed
wireless sensor network. The average weighted Kullback-Leibler divergence,
which is a function of the unknown drift parameters, is employed as the cost to
measure the discrepancy between the fused posterior distribution and the local
distribution at each sensor. Further, a reasonable approximation of the cost is
proposed and an online technique is introduced to minimize the approximated
cost function with respect to the drift parameters stored in each node. The
remarkable features of the proposed algorithm are that it needs no additional
data exchanges, slightly increased memory space and computational load
comparable to the standard consensus-based Kalman filter. Finally, the
effectiveness of the proposed algorithm is demonstrated through simulation
experiments on both a tree network and a network with cycles as well as for
both linear and nonlinear sensors
Distributed Joint Sensor Registration and Multitarget Tracking Via Sensor Network
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