3 research outputs found
Multiple Model Poisson Multi-Bernoulli Mixture Filter for Maneuvering Targets
The Poisson multi-Bernoulli mixture (PMBM) filter is conjugate prior composed
of the union of a Poisson point process (PPP) and a multi-Bernoulli mixture
(MBM). In this paper, a new PMBM filter for tracking multiple targets with
randomly time-varying dynamics under multiple model (MM) is considered. The
proposed MM-PMBM filter uses extends the single-model PMBM filter recursion to
multiple motion models by using the jump Markov system (JMS). The performance
of the proposed algorithm is examined and compared with the MM-MB filter. The
simulation results demonstrate that the proposed MM-PMBM filter outperforms the
MM-MB filter in terms of the tracking accuracy, including the target states and
cardinality, especially for the scenerio with low detection probability.
Moreover, the comparisons for the variations of detection probability and
standard derivation of measurement noise are also tested via simulation
experiments.Comment: 10 pages, 3 figure
A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors
We propose a method for tracking an unknown number of targets based on
measurements provided by multiple sensors. Our method achieves low
computational complexity and excellent scalability by running belief
propagation on a suitably devised factor graph. A redundant formulation of data
association uncertainty and the use of "augmented target states" including
binary target indicators make it possible to exploit statistical independencies
for a drastic reduction of complexity. An increase in the number of targets,
sensors, or measurements leads to additional variable nodes in the factor graph
but not to higher dimensions of the messages. As a consequence, the complexity
of our method scales only quadratically in the number of targets, linearly in
the number of sensors, and linearly in the number of measurements per sensors.
The performance of the method compares well with that of previously proposed
methods, including methods with a less favorable scaling behavior. In
particular, our method can outperform multisensor versions of the probability
hypothesis density (PHD) filter, the cardinalized PHD filter, and the
multi-Bernoulli filter.Comment: 13 pages, 8 figur
Scalable Detection and Tracking of Geometric Extended Objects
Multiobject tracking provides situational awareness that enables new
applications for modern convenience, public safety, and homeland security. This
paper presents a factor graph formulation and a particle-based sum-product
algorithm (SPA) for scalable detection and tracking of extended objects. The
proposed method dynamically introduces states of newly detected objects,
efficiently performs probabilistic multiple-measurement to object association,
and jointly infers the geometric shapes of objects. Scalable extended object
tracking (EOT) is enabled by modeling association uncertainty by
measurement-oriented association variables and newly detected objects by a
Poisson birth process. Contrary to conventional EOT methods, a fully
particle-based approach makes it possible to describe different geometric
object shapes. The proposed method can reliably detect, localize, and track a
large number of closely-spaced extended objects without gating and clustering
of measurements. We demonstrate significant performance advantages of our
approach compared to the recently introduced Poisson multi-Bernoulli mixture
filter. In particular, we consider a simulated scenarios with up to twenty
closely-spaced objects and a real autonomous driving application where
measurements are captured by a lidar sensor.Comment: 29 pages, 8 figures, 2 table