  Multisensor track-to-track fusion for target tracking involves two primary
operations: track association and estimation fusion. For estimation fusion,
lossless measurement transformation of sensor measurements has been proposed
for single target tracking. In this paper, we investigate track association
which is a fundamental and important problem for multitarget tracking. First,
since the optimal track association problem is a multi-dimensional assignment
(MDA) problem, we demonstrate that MDA-based data association (with and without
prior track information) using linear transformations of track measurements is
lossless, and is equivalent to that using raw track measurements. Second,
recent superior scalability and performance of belief propagation (BP)
algorithms enable new real-time applications of multitarget tracking with
resource-limited devices. Thus, we present a BP-based multisensor track
association method with transformed measurements and show that it is equivalent
to that with raw measurements. Third, considering communication constraints, it
is more beneficial for local sensors to send in compressed data. Two analytical
lossless transformations for track association are provided, and it is shown
that their communication requirements from each sensor to the fusion center are
less than those of fusion with raw track measurements. Numerical examples for
tracking an unknown number of targets verify that track association with
transformed track measurements has the same performance as that with raw
measurements and requires fewer communication bandwidths.
