Next-generation wireless networks are expected to be highly heterogeneous, multilayered,
with embedded intelligence at both the core and edge of the network. In such a context …

Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive
estimation of dynamic systems, recently emerged from the random set theoretical framework …

This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli
(GLMB) filter by combining the prediction and update into a single step. In contrast to an …

This paper proposes a generalization of the multi-Bernoulli filter called the labeled multi-
Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not …

An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled
Multi-Bernoulli (δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled …

The objective of multi-object estimation is to simultaneously estimate the number of objects
and their states from a set of observations in the presence of data association uncertainty …

Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets
and their states or trajectories from noisy sensor measurements. MTT has a long history …

Multi-frame track-before-detect (MF-TBD) is a model-based batch processing method.
Assuming a particular model for the evolution of target states (eg a constant velocity model) …

In multiobject inference, the multiobject probability density captures the uncertainty in the
number and the states of the objects as well as the statistical dependence between the …

The paper addresses the problem of distributed multi-target tracking (MTT) in a network of
sensors having different fields of view (FoVs). Probability hypothesis density (PHD) filters are …