Particle filters for state estimation of jump Markov linear systems

Doucet, Arnaud; Gordon, Neil; Krishnamurthy, Vikram

doi:10.1109/78.905890

Cited by 619 publications

(412 citation statements)

References 32 publications

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“…Generate the particle filterˆ 0:N according to (15) or a more sophisticated scheme (Doucet, Gordon, & Krishnamurthy, 2001) 3. Substitute (X 0:N , Y 1:N ), A 1:N,k andˆ 0:N into (13) to obtain ∇J (A 1:N,k ):…”

Section: Keeping In Mind Thatmentioning

confidence: 99%

Simulation-based optimal sensor scheduling with application to observer trajectory planning

Singh

Kantas

et al. 2007

Automatica

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The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions. ᭧

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Section: Keeping In Mind Thatmentioning

confidence: 99%

Simulation-based optimal sensor scheduling with application to observer trajectory planning

Singh

Kantas

et al. 2007

Automatica

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“…In this case, the optimal filter density can be computed using a weighted mode-matched sequence of Kalman filters, one for each trajectory of modes. Since the optimal filtering density cannot be directly computed due the computational complexity which grows exponentially with time, a number of approximation techniques have been developed to deal with these systems, such as the IMM estimator [3], [10], Gaussian mixture reduction techniques [13] and particle filtering methods [8]. An excellent survey of all these techniques can be found in [11].…”

Section: Introductionmentioning

confidence: 99%

Stochastic filtering in jump systems with state dependent mode transitions

Capponi

Pilotto

2009

2009 American Control Conference

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Abstract-We introduce a new methodology to construct a Gaussian mixture approximation to the true filter density in hybrid Markovian switching systems. We relax the assumption that the mode transition process is a Markov chain and allow it to depend on the actual and unobservable state of the system. The main feature of the method is that the Gaussian densities used in the approximation are selected as the solution of a convex programming problem which trades off sparsity of the solution with goodness of fit. A meaningful example shows that the proposed method can outperform the widely used interacting multiple model (IMM) filter in terms of accuracy at the expenses of an increase in computational time.

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“…Section 2 summarizes the Bayesian formulation of the JTC problem according to [4,13,22]. Section 3 presents the developed MM particle filter, MKF, and delayed-pilot MKF using speed and acceleration constraints.…”

Section: Introductionmentioning

confidence: 99%

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

Angelova

Mihaylova

2006

Digital Signal Processing

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This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a prior information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the classification process. The two designed estimators are compared and evaluated over rather complex target scenarios. The results demonstrate the usefulness of the proposed scheme for the incorporation of additional speed information. Both filters illustrate the opportunity of the particle filtering and mixture Kalman filtering to incorporate constraints in a natural way, providing reliable tracking and correct classification. Future observations contain valuable information about the current state of the dynamic systems. In the framework of the MKF, an algorithm for delayed estimation is designed for improving the current modal state estimate. It is used as an additional, more reliable information in resolving complicated classification situations.

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Particle filters for state estimation of jump Markov linear systems

Cited by 619 publications

References 32 publications

Simulation-based optimal sensor scheduling with application to observer trajectory planning

Simulation-based optimal sensor scheduling with application to observer trajectory planning

Stochastic filtering in jump systems with state dependent mode transitions

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

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