733 research outputs found
A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation
State space models in which the system state is a finite set--called the
multi-object state--have generated considerable interest in recent years.
Smoothing for state space models provides better estimation performance than
filtering by using the full posterior rather than the filtering density. In
multi-object state estimation, the Bayes multi-object filtering recursion
admits an analytic solution known as the Generalized Labeled Multi-Bernoulli
(GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate
the multi-object posterior. We also propose an implementation of this so-called
multi-scan GLMB posterior recursion using a similar approach to the GLMB filter
implementation
Linear Complexity Gibbs Sampling for Generalized Labeled Multi-Bernoulli Filtering
Generalized Labeled Multi-Bernoulli (GLMB) densities arise in a host of
multi-object system applications analogous to Gaussians in single-object
filtering. However, computing the GLMB filtering density requires solving
NP-hard problems. To alleviate this computational bottleneck, we develop a
linear complexity Gibbs sampling framework for GLMB density computation.
Specifically, we propose a tempered Gibbs sampler that exploits the structure
of the GLMB filtering density to achieve an complexity,
where is the number of iterations of the algorithm, and are the
number hypothesized objects and measurements. This innovation enables an
complexity implementation of the GLMB filter.
Convergence of the proposed Gibbs sampler is established and numerical studies
are presented to validate the proposed GLMB filter implementation
On Gibbs Sampling Architecture for Labeled Random Finite Sets Multi-Object Tracking
Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms
because of its simplicity, scalability, and wide applicability within many
fields of statistics, science, and engineering. In the labeled random finite
sets literature, Gibbs sampling procedures have recently been applied to
efficiently truncate the single-sensor and multi-sensor -generalized
labeled multi-Bernoulli posterior density as well as the multi-sensor adaptive
labeled multi-Bernoulli birth distribution. However, only a limited discussion
has been provided regarding key Gibbs sampler architecture details including
the Markov chain Monte Carlo sample generation technique and early termination
criteria. This paper begins with a brief background on Markov chain Monte Carlo
methods and a review of the Gibbs sampler implementations proposed for labeled
random finite sets filters. Next, we propose a short chain, multi-simulation
sample generation technique that is well suited for these applications and
enables a parallel processing implementation. Additionally, we present two
heuristic early termination criteria that achieve similar sampling performance
with substantially fewer Markov chain observations. Finally, the benefits of
the proposed Gibbs samplers are demonstrated via two Monte Carlo simulations.Comment: Accepted to the 2023 Proc. IEEE 26th Int. Conf. Inf. Fusio
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
A Moving Window Based Approach to Multi-scan Multi-Target Tracking
Multi-target state estimation refers to estimating the number of targets and
their trajectories in a surveillance area using measurements contaminated with
noise and clutter. In the Bayesian paradigm, the most common approach to
multi-target estimation is by recursively propagating the multi-target
filtering density, updating it with current measurements set at each timestep.
In comparison, multi-target smoothing uses all measurements up to current
timestep and recursively propagates the entire history of multi-target state
using the multi-target posterior density. The recent Generalized Labeled
Multi-Bernoulli (GLMB) smoother is an analytic recursion that propagate the
labeled multi-object posterior by recursively updating labels to measurement
association maps from the beginning to current timestep. In this paper, we
propose a moving window based solution for multi-target tracking using the GLMB
smoother, so that only those association maps in a window (consisting of latest
maps) get updated, resulting in an efficient approximate solution suitable for
practical implementations
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