A Poisson multi-Bernoulli mixture filter with spawning based on Kullback–Leibler divergence minimization

Su, Zhenzhen; Ji, Hongbing; Zhang, Yongquan

doi:10.1016/j.cja.2020.11.015

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…In general, the single branch density p i,j,b k|k (•) for the i-th Bernoulli tree, j-th branch and local hypothesis b i,j , see (21), can be written as…”

Section: Single-branch Densitiesmentioning

confidence: 99%

“…These filters have been implemented with the parameters (see Section V-D): N h = 100, Γ mbm = 10 −4 , Γ p = 10 −4 , Γ b = 10 −4 , Γ a = 10 −4 , gating threshold 15, Γ d = 0.4, and L ∈ {1, 5}. We have also implemented the PMBM filter with spawning (S-PMBM) in [21], which uses recycling [35] to add the information on spawned targets into the PPP. In addition, we have implemented two filters that do not take into account spawning: the PMBM [11], [12] and the trajectory PMBM (TPMBM) filter [10], [30].…”

Section: Simulationsmentioning

confidence: 99%

“…included in the dynamic model by adding a spawning model [6], [17]. Several approximate filters have been developed to estimate the current set of targets with spawning, for example, the probability hypothesis density (PHD) filter [6], [18], the cardinality PHD (CPHD) filter [19], [20], the PMBM filter [21] and the generalised labelled multi-Bernoulli (GLMB) filter [22]- [24], which provides genealogy information on the targets.…”

Section: Introductionmentioning

confidence: 99%

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Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories

García-Fernández,

Svensson

2021

Preprint

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This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter on the space of sets of tree trajectories for multiple target tracking with spawning targets. A tree trajectory contains all trajectory information of a target and its descendants, which appear due to the spawning process. Each tree contains a set of branches, where each branch has trajectory information of a target or one of the descendants and its genealogy. For the standard dynamic and measurement models with multi-Bernoulli spawning, the posterior is a PMBM density, with each Bernoulli having information on a potential tree trajectory. To enable a computationally efficient implementation, we derive an approximate PMBM filter in which each Bernoulli tree trajectory has multi-Bernoulli branches obtained by minimising the Kullback-Leibler divergence. The resulting filter improves tracking performance of state-of-the-art algorithms in a simulated scenario.

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“…In general, the single branch density p i,j,b k|k (•) for the i-th Bernoulli tree, j-th branch and local hypothesis b i,j , see (21), can be written as…”

Section: Single-branch Densitiesmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories

García-Fernández,

Svensson

2021

Preprint

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show abstract

“…ing targets with a much lower SNR than traditional methods. A few TBD algorithms have been developed based on the recursive Bayesian estimator, such as the particle filter (PF) [25−27], the probability hypothesis density (PHD) filter [28,29], and the multi-Bernoulli filter [30,31]. Due to its simple implementation and good performance at low SNR conditions, the PF and its variations have been widely used [24].…”

Section: Introductionmentioning

confidence: 99%

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

Zeng

Jiadong

Wang

et al. 2023

J. of Syst. Eng. Electron.

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Global Navigation Satellite System (GNSS)-based passive radar (GBPR) has been widely used in remote sensing applications. However, for moving target detection (MTD), the quadratic phase error (QPE) introduced by the non-cooperative target motion is usually difficult to be compensated, as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective. Consequently, the moving target in GBPR image is usually defocused, which aggravates the difficulty of target detection even further. In this paper, a spawning particle filter (SPF) is proposed for defocused MTD. Firstly, the measurement model and the likelihood ratio function (LRF) of the defocused point-like target image are deduced. Then, a spawning particle set is generated for subsequent target detection, with reference to traditional particles in particle filter (PF) as their parent. After that, based on the PF estimator, the SPF algorithm and its sequential Monte Carlo (SMC) implementation are proposed with a novel amplitude estimation method to decrease the target state dimension. Finally, the effectiveness of the proposed SPF is demonstrated by numerical simulations and preliminary experimental results, showing that the target range and Doppler can be estimated accurately.

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A DNN-Based GLMB Algorithm for Multi-sensor Multi-target Tracking with Dense Group Clutter

Zhang,

Shang,

et al. 2024

Lecture Notes in Electrical Engineering

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A Poisson multi-Bernoulli mixture filter with spawning based on Kullback–Leibler divergence minimization

Cited by 13 publications

References 29 publications

Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories

Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories

A spawning particle filter for defocused moving target detection in GNSS-based passive radar

A DNN-Based GLMB Algorithm for Multi-sensor Multi-target Tracking with Dense Group Clutter

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