61 research outputs found
Robust Multi-target Tracking with Bootstrapped-GLMB Filter
This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters
Robust Multi-Object Tracking: A Labeled Random Finite Set Approach
The labeled random finite set based generalized multi-Bernoulli filter is a tractable analytic solution for the multi-object tracking problem. The robustness of this filter is dependent on certain knowledge regarding the multi-object system being available to the filter. This dissertation presents techniques for robust tracking, constructed upon the labeled random finite set framework, where complete information regarding the system is unavailable
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
A CPHD Filter for Tracking With Spawning Models
In some applications of multi-target tracking, appearing targets are suitably modeled as spawning from existing targets. However, in the original formulation of the cardinalized probability hypothesis density (CPHD) filter, this type of model is not supported; instead appearing targets are modeled by spontaneous birth only. In this paper we derive the necessary equations for a CPHD filter for the case when the process model also includes target spawning. For this generalized filter, the cardinality prediction formula might become computationally intractable for general spawning models. However, when the cardinality distribution of the spawning targets is either Bernoulli or Poisson, we derive expressions that are practical and computationally efficient. Simulations show that the proposed filter responds faster to a change in target number due to spawned targets than the original CPHD filter. In addition, the performance of the filter, considering the optimal subpattern assignment (OSPA), is improved when having an explicit spawning model
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
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
- …