Efficient update of persistent particles in the SMC-PHD filter

Ristić, Branko

doi:10.1109/icassp.2015.7178746

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…How to construct these importance densities has been a topic of intensive research in the last decade, see [50], [49], [51], [52]. The method described below mainly follows [53].…”

Section: The Particle Methods Applied To Phd Filteringmentioning

confidence: 99%

“…A slight modification of any standard particle filter can implement (53). This step is carried out in line 11 where PFU stands for particle-filter update (to be explained later).…”

Section: The Particle Methods Applied To Phd Filteringmentioning

confidence: 99%

“…5. A PFU routine based on progressive correction is discussed in [53]. Let the cluster C k|k−1 (z) consist of M weighted particles, {w…”

Section: The Particle Methods Applied To Phd Filteringmentioning

confidence: 99%

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An overview of particle methods for random finite set models

2016

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This overview paper describes the particle methods developed for the implementation of the a class of Bayes filters formulated using the random finite set formalism. It is primarily intended for the readership already familiar with the particle methods in the context of the standard Bayes filter. The focus in on the Bernoulli particle filter, the probability hypothesis density (PHD) particle filter and the generalised labelled multi-Bernoulli (GLMB) particle filter. The performance of the described filters is demonstrated in the context of bearings-only target tracking application.

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“…How to construct these importance densities has been a topic of intensive research in the last decade, see [50], [49], [51], [52]. The method described below mainly follows [53].…”

Section: The Particle Methods Applied To Phd Filteringmentioning

confidence: 99%

“…A slight modification of any standard particle filter can implement (53). This step is carried out in line 11 where PFU stands for particle-filter update (to be explained later).…”

Section: The Particle Methods Applied To Phd Filteringmentioning

confidence: 99%

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An overview of particle methods for random finite set models

2016

Self Cite

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“…It needs to be chosen in a way that the particles on the undetected persistent whistles, that are collected in a cluster C k|k−1 (∅), are not eliminated. Following recommendations elsewhere 44 , this study used ξ = 100(1 − p D )/M where M denotes the number of particles in C k|k−1 (∅).…”

Section: Other Parametersmentioning

confidence: 99%

Automated extraction of dolphin whistles—A sequential Monte Carlo probability hypothesis density approach

Gruden

White

2020

The Journal of the Acoustical Society of America

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The need for automated methods to detect and extract marine mammal vocalizations from acoustic data has increased in the last few decades due to the increased availability of long-term recording systems. Automated dolphin whistle extraction represents a challenging problem due to the time-varying number of overlapping whistles present in, potentially, noisy recordings. Typical methods utilize image processing techniques or single target tracking, but often result in fragmentation of whistle contours and/or partial whistle detection. This study casts the problem into a more general statistical multi-target tracking framework, and uses the probability hypothesis density (PHD) filter as a practical approximation to the optimal Bayesian multi-target filter. In particular, a particle version, referred to as a Sequential Monte Carlo PHD (SMC-PHD) filter, is adapted for frequency tracking and specific models are developed for this application. Based on these models, two versions of the SMC-PHD filter are proposed and their performance is investigated on an extensive real-world dataset of dolphin acoustic recordings. The proposed filters are shown to be efficient tools for automated extraction of whistles, suitable for real-time implementation.

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“…As an alternative to the association-based methods, the random finite sets (RFS) approach is an emerging technique to multi-target tracking (MTT), and the resulting optimal multi-target Bayes filter provides a rigorous theoretical basis for many novel multi-target filters [ 4 , 5 , 6 ]. In this context, the probability hypothesis density (PHD) filter [ 4 ] which is derived via first-order moment approximation of the multi-target posterior density, and its implementations such as sequential Monte Carlo PHD (SMC-PHD) filter [ 7 , 8 ] and Gaussian mixture PHD (GM-PHD) filter [ 9 ], have been widely studied in the area of MTT over the last decade [ 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Target State Extraction for the SMC-PHD Filter

Wang

2016

Sensors

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The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been demonstrated to be a favorable method for multi-target tracking. However, the time-varying target states need to be extracted from the particle approximation of the posterior PHD, which is difficult to implement due to the unknown relations between the large amount of particles and the PHD peaks representing potential target locations. To address this problem, a novel multi-target state extraction algorithm is proposed in this paper. By exploiting the information of measurements and particle likelihoods in the filtering stage, we propose a validation mechanism which aims at selecting effective measurements and particles corresponding to detected targets. Subsequently, the state estimates of the detected and undetected targets are performed separately: the former are obtained from the particle clusters directed by effective measurements, while the latter are obtained from the particles corresponding to undetected targets via clustering method. Simulation results demonstrate that the proposed method yields better estimation accuracy and reliability compared to existing methods.

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Efficient update of persistent particles in the SMC-PHD filter

Cited by 5 publications

References 18 publications

An overview of particle methods for random finite set models

An overview of particle methods for random finite set models

Automated extraction of dolphin whistles—A sequential Monte Carlo probability hypothesis density approach

Multi-Target State Extraction for the SMC-PHD Filter

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