  Passive monitoring of acoustic or radio sources has important applications in
modern convenience, public safety, and surveillance. A key task in passive
monitoring is multiobject tracking (MOT). This paper presents a Bayesian method
for multisensor MOT for challenging tracking problems where the object states
are high-dimensional, and the measurements follow a nonlinear model. Our method
is developed in the framework of factor graphs and the sum-product algorithm
(SPA) and implemented using random samples or "particles". The multimodal
probability density functions (pdfs) provided by the SPA are effectively
represented by a Gaussian mixture model (GMM). To perform the operations of the
SPA with improved sample efficiency, we make use of Particle flow (PFL). Here,
particles are migrated towards regions of high likelihood based on the solution
of a partial differential equation. This makes it possible to obtain good
object detection and tracking performance even in challenging multisensor MOT
scenarios with single sensor measurements that have a lower dimension than the
object positions. We perform a numerical evaluation in a passive acoustic
monitoring scenario where multiple sources are tracked in 3-D from 1-D
time-difference-of-arrival (TDOA) measurements provided by pairs of
hydrophones. Our numerical results demonstrate favorable detection and
estimation accuracy compared to state-of-the-art reference techniques.
