14 research outputs found
Sensor management for multi-target tracking via multi-bernoulli filtering
In multi-object stochastic systems, the issue of sensor management is a theoretically and computationally challenging problem. In this paper, we present a novel random finite set (RFS) approach to the multi-target sensor management problem within the partially observed Markov decision process (POMDP) framework. The multi-target state is modelled as a multi-Bernoulli RFS, and the multi-Bernoulli filter is used in conjunction with two different control objectives: maximizing the expected Rényi divergence between the predicted and updated densities, and minimizing the expected posterior cardinality variance. Numerical studies are presented in two scenarios where a mobile sensor tracks five moving targets with different levels of observability
Information theoretic approach to robust multi-Bernoulli sensor control
A novel sensor control solution is presented, formulated within a
Multi-Bernoulli-based multi-target tracking framework. The proposed method is
especially designed for the general multi-target tracking case, where no prior
knowledge of the clutter distribution or the probability of detection profile
are available. In an information theoretic approach, our method makes use of
R\`{e}nyi divergence as the reward function to be maximized for finding the
optimal sensor control command at each step. We devise a Monte Carlo sampling
method for computation of the reward. Simulation results demonstrate successful
performance of the proposed method in a challenging scenario involving five
targets maneuvering in a relatively uncertain space with unknown
distance-dependent clutter rate and probability of detection
Sensor Control for Multi-Object Tracking Using Labeled Multi-Bernoulli Filter
The recently developed labeled multi-Bernoulli (LMB) filter uses better
approximations in its update step, compared to the unlabeled multi-Bernoulli
filters, and more importantly, it provides us with not only the estimates for
the number of targets and their states, but also with labels for existing
tracks. This paper presents a novel sensor-control method to be used for
optimal multi-target tracking within the LMB filter. The proposed method uses a
task-driven cost function in which both the state estimation errors and
cardinality estimation errors are taken into consideration. Simulation results
demonstrate that the proposed method can successfully guide a mobile sensor in
a challenging multi-target tracking scenario
Exploiting Bounded Sensor Field-of-View Geometry in Tracking and Sensor Planning Problems
In search-detect-track problems, knowledge of where objects were not seen can
be as valuable as knowledge of where objects were seen. Exploiting the sensor's
known sensing extents, or field-of-view (FoV), this type of evidence can be
incorporated in a Bayesian framework to improve tracking accuracy and form
better sensor schedules. This paper presents new techniques for incorporating
bounded FoV inclusion/exclusion evidence in object state densities and
multi-object cardinality distributions. Some examples of how the proposed
techniques may be applied to tracking and sensor planning problems are given
Multi-Bernoulli Sensor-Control via Minimization of Expected Estimation Errors
This paper presents a sensor-control method for choosing the best next state
of the sensor(s), that provide(s) accurate estimation results in a multi-target
tracking application. The proposed solution is formulated for a multi-Bernoulli
filter and works via minimization of a new estimation error-based cost
function. Simulation results demonstrate that the proposed method can
outperform the state-of-the-art methods in terms of computation time and
robustness to clutter while delivering similar accuracy