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 …

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 standard formulation of the probability hypothesis density (PHD) and cardinalised PHD
(CPHD) filters assumes that the target birth intensity is known a priori. In situations where the …

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 …

This paper proposes an efficient implementation of the multi-sensor generalized labeled
multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation …

The paper makes two contributions. First, a new formulation of the PHD filter which
distinguishes between persistent and newborn objects is presented. This formulation results …

The context is sensor control for multi-object Bayes filtering in the framework of partially
observed Markov decision processes (POMDPs). The current information state is …

In this paper we present a general solution for multi-target tracking with superpositional
measurements. Measurements that are functions of the sum of the contributions of the …

摘要概率假设密度(Probability hypothesis density, PHD) 滤波方法在多目标跟踪, 交通管制,
图像处理以及多传感器管理等领域得到了广泛关注. 本文对基于PHD 滤波方法的多目标跟踪 …

As a result of the dependence worldwide on satellite technology, it is now necessary to use
advanced multi-target tracking algorithms for space debris tracking systems to maintain …