  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.
