  Multitarget tracking in the interference environments suffers from the
nonuniform, unknown and time-varying clutter, resulting in dramatic performance
deterioration. We address this challenge by proposing a robust multitarget
tracking algorithm, which estimates the states of clutter and targets
simultaneously by the message-passing (MP) approach. We define the
non-homogeneous clutter with a finite mixture model containing a uniform
component and multiple nonuniform components. The measured signal strength is
utilized to estimate the mean signal-to-noise ratio (SNR) of targets and the
mean clutter-to-noise ratio (CNR) of clutter, which are then used as additional
feature information of targets and clutter to improve the performance of
discrimination of targets from clutter. We also present a hybrid data
association which can reason over correspondence between targets, clutter, and
measurements. Then, a unified MP algorithm is used to infer the marginal
posterior probability distributions of targets, clutter, and data association
by splitting the joint probability distribution into a mean-field approximate
part and a belief propagation part. As a result, a closed-loop iterative
optimization of the posterior probability distribution can be obtained, which
can effectively deal with the coupling between target tracking, clutter
estimation and data association. Simulation results demonstrate the performance
superiority and robustness of the proposed multitarget tracking algorithm
compared with the probability hypothesis density (PHD) filter and the
cardinalized PHD (CPHD) filter.
