Particle Filtering With Dependent Noise Processes

Saha, Saikat; Gustafsson, Fredrik

doi:10.1109/tsp.2012.2202653

Cited by 43 publications

(31 citation statements)

References 20 publications

(21 reference statements)

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“…The practical interest of some particular PMC models has been pointed out in a recent contribution [22]. In addition, we present another practical interest of PMC models: we show that linear and Gaussian PMC models enable us to to keep the physical constraints of a given HMC model while relaxing its independence assumptions.…”

Section: Applications and Simulationsmentioning

confidence: 71%

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Bayesian Multi-Object Filtering for Pairwise Markov Chains

Petetin

Desbouvries

2013

IEEE Trans. Signal Process.

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Abstract-Random Finite Sets (RFS) are recent tools for addressing the multi-object filtering problem. The Probability Hypothesis Density (PHD) Filter is an approximation of the multiobject Bayesian filter which results from the RFS formulation of the problem and has been used in many applications. In the RFS framework, it is assumed that each target and associated observation follow a Hidden Markov Chain (HMC) model. HMCs conveniently describe some physical properties of practical interest for practitioners, but they also implicitly imply restrictive independence properties which, in practice, may not be satisfied by data. In this paper, we show that these structural limitations of HMC models can somehow be relaxed by embedding them into the more general class of Pairwise Markov Chain (PMC) models. We thus focus on the computation of the PHD filter in a PMC framework, and we propose a practical implementation of the PHD filter for a particular class of PMC models.

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Section: Applications and Simulationsmentioning

confidence: 71%

“…These pdfs are chosen according to the physical tracking problem at hand, and to the sensors which are used to track the targets. However, the Markovian and independence assumptions which are implicit in HMC modeling may not be satisfied in practice, i.e., the data do not necessarily fit the strong assumptions underlying these models [22].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Multi-Object Filtering for Pairwise Markov Chains

Petetin

Desbouvries

2013

IEEE Trans. Signal Process.

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“…This influence was first considered in [31] and was further popularized in [43]. This framework is extended to the dependent noise processes in [37]. The interconnection between the two parts of the state space, x n k and x l k is best illustrated in a graphical model as in Figure 1(b).…”

Section: Mixed Linear/nonlinear Gaussian State-space Modelmentioning

confidence: 99%

“…In the particle filtering framework, this leads to another Rao-Blackwellization approach. One typical application of the resulting RBPF is a noise adaptive particle filtering for a general statespace model [32,37,39]. A brief description of the general approach is given below.…”

Section: Conditionally Conjugate Latent Process Modelmentioning

confidence: 99%

A Bayesian Uncertainty Analysis for Nonignorable Nonresponse

Saha¹,

Hendeby²,

Gustafsson³

2015

Current Trends in Bayesian Methodology With Applications

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The discrete time general state-space model is a flexible framework to deal with the nonlinear and/or non-Gaussian time series problems. However, the associated (Bayesian) inference problems are often intractable. Additionally, for many applications of interest, the inference solutions are required to be recursive over time. The particle filter (PF) is a popular class of Monte Carlo based numerical methods to deal with such problems in real time. However, PF is known to be computationally expensive and does not scale well with the problem dimensions. If a part of the state space is analytically tractable conditioned on the remaining part, the Monte Carlo based estimation is then confined to a space of lower dimension, resulting in an estimation method known as the Rao-Blackwellized particle filter (RBPF). In this chapter, we present a brief review of Rao-Blackwellized particle filtering. Especially, we outline a set of popular conditional tractable structures admitting such Rao-Blackwellization in practice. For some special and/or relatively new cases, we also provide reasonably detailed descriptions.We confine our presentation mostly to the practitioners’ point of view.

This is a review article on the models admitting Rao-Blackwellization in particle filtering.

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“…Each of these trajectories is represented at a given discrete time using a single number (particles). Using information obtained from consecutive measurements of each trajectory a weight is assigned, which determines the probability that a given trajectory represents the actual trajectory [40][41][42][43][44][45].…”

Section: The Particle Filtermentioning

confidence: 99%

The dynamics of the human arm with an observer for the capture of body motion parameters

Babiarz¹,

Bieda²,

Jaskot³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. The paper presents an analysis of a mathematical model of the human arm dynamics in terms of observability. The purpose of the performed experiments is the selection of an observer for the possibility of arm tracking. The arm model is based on the two-link manipulator moving horizontally and vertically. For the study a model was linearized and the model part responsible for the work of human muscles was omitted. The experimental part involved simulated measurements of the motion parameters that imitate real-IMU (Inertial Measurement Unit) measurements. Finally, the simulation results using the observer in the form of a Kalman filter and the particle filter have been presented.

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Particle Filtering With Dependent Noise Processes

Cited by 43 publications

References 20 publications

Bayesian Multi-Object Filtering for Pairwise Markov Chains

Bayesian Multi-Object Filtering for Pairwise Markov Chains

A Bayesian Uncertainty Analysis for Nonignorable Nonresponse

The dynamics of the human arm with an observer for the capture of body motion parameters

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