Efficient particle filtering for jump Markov systems

Andrieu,; Davy,; Doucet,

doi:10.1109/icassp.2002.1006070

Cited by 19 publications

(31 citation statements)

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“…Similar techniques can be applied to approximate V (2),opt n (x n−1 ). Other suggestions can be found in [39], [45].…”

Section: Design Of Proposal Distributionsmentioning

confidence: 99%

“…We use the UT to approximate each V (1),opt n,p in the same manner that it is used to approximate the predictive likelihood in [45]. This requires sigma points and associated weights to be calculated for each particle and the mean of the normalized birth intensity, at each iteration.…”

Section: B Examplementioning

confidence: 99%

“…Therefore point (A3) is achieved. See [36], [45] for several possibilities and section VI for specific details of use of the UT.…”

Section: Design Of Proposal Distributionsmentioning

confidence: 99%

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Auxiliary Particle Implementation of the Probability Hypothesis Density Filter

Whiteley

Singh

Godsill

2007

2007 5th International Symposium on Image and Signal Processing and Analysis

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Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.

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“…Similar techniques can be applied to approximate V (2),opt n (x n−1 ). Other suggestions can be found in [39], [45].…”

Section: Design Of Proposal Distributionsmentioning

confidence: 99%

Section: B Examplementioning

confidence: 99%

See 1 more Smart Citation

Auxiliary Particle Implementation of the Probability Hypothesis Density Filter

Whiteley

Singh

Godsill

2007

2007 5th International Symposium on Image and Signal Processing and Analysis

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“…In this paper, we generalise this framework and show that the problem can be modelled as a jump Markov system (JMS) [15][16][17][18]. JMSs can be regarded as a generalisation of Hidden Markov Models (HMMs).…”

Section: Introductionmentioning

confidence: 99%

“…This reduces the space and computational requirements of the particle filtering method. The novelty of our approach is to combine JMS as in [18] and particle filtering for superimposed event detection and to exploit the properties, which are specific to this type of problem.…”

Section: Introductionmentioning

confidence: 99%

Superimposed event detection by particle filters

Urfalıoḡlu

Kuruoğlu

Çetin

2011

IET Signal Process.

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In this study, the authors consider online detection and separation of superimposed events by applying particle filtering. They observe only a single-channel superimposed signal, which consists of a background signal and one or more event signals in the discrete-time domain. It is assumed that the signals are statistically independent and can be described by random processes with known parametric models. The activation and deactivation times of event signals are assumed to be unknown. This problem can be described as a jump Markov system (JMS) in which all signals are estimated simultaneously. In a JMS, states contain additional parameters to identify models. However, for superimposed event detection, the authors show that the underlying JMS-based particle-filtering method can be reduced to a standard Markov chain method without additional parameters. Numerical experiments using real-world sound processing data demonstrate the effectiveness of their approach.

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Improved target tracking with road network information

Orguner

Schön

Gustafsson

2009

2009 IEEE Aerospace Conference

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Abstract-In this paper we consider the problem of tracking targets, which can move both on-road and off-road, with particle filters utilizing the road-network information. It is argued that the constraints like speed-limits and/or oneway roads generally incorporated into on-road motion models make it necessary to consider additional high-bandwidth off-road motion models. This is true even if the targets under consideration are only allowed to move on-road due to the possibility of imperfect road-map information and drivers violating the traffic rules. The particle filters currently used struggles during sharp mode transitions, with poor estimation quality as a result. This is due to the fact the number of particles allocated to each motion mode is varying according to the mode probabilities. A recently proposed interacting multiple model (IMM) particle filtering algorithm, which keeps the number of particles in each mode constant irrespective of the mode probabilities, is applied to this problem and its performance is compared to a previously existing algorithm. The results of the simulations on a challenging bearing-only tracking scenario show that the proposed algorithm, unlike the previously existing algorithm, can achieve good performance even under the sharpest mode transitions.

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Efficient particle filtering for jump Markov systems

Cited by 19 publications

References 0 publications

Auxiliary Particle Implementation of the Probability Hypothesis Density Filter

Auxiliary Particle Implementation of the Probability Hypothesis Density Filter

Superimposed event detection by particle filters

Improved target tracking with road network information

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