  This paper is concerned with the problem of distributed extended object
tracking, which aims to collaboratively estimate the state and extension of an
object by a network of nodes. In traditional tracking applications, most
approaches consider an object as a point source of measurements due to limited
sensor resolution capabilities. Recently, some studies consider the extended
objects, which are spatially structured, i.e., multiple resolution cells are
occupied by an object. In this setting, multiple measurements are generated by
each object per time step. In this paper, we present a Bayesian model for
extended object tracking problem in a sensor network. In this model, the object
extension is represented by a symmetric positive definite random matrix, and we
assume that the measurement noise exists but is unknown. Using this Bayesian
model, we first propose a novel centralized algorithm for extended object
tracking based on variational Bayesian methods. Then, we extend it to the
distributed scenario based on the alternating direction method of multipliers
(ADMM) technique. The proposed algorithms can simultaneously estimate the
extended object state (the kinematic state and extension) and the measurement
noise covariance. Simulations on both extended object tracking and group target
tracking are given to verify the effectiveness of the proposed model and
algorithms.
