Particle Methods for Change Detection, System Identification, and Control

Andrieu, Christophe; Doucet, Arnaud; Singh, Sumeetpal S.; Tadic, V.B.

doi:10.1109/jproc.2003.823142

Cited by 280 publications

(249 citation statements)

References 38 publications

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“…For example, a traveller may switch from walking to riding the bus, and both the dynamics and observations will vary accordingly. The change detection problem [3] can be also considered as an interesting particular case. For instance, see the numerical example in Section 6.1.…”

Section: Mapf For Time-varying Modelsmentioning

confidence: 99%

Cooperative parallel particle filters for online model selection and applications to urban mobility

Martino

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Elvira

et al. 2017

Digital Signal Processing

136

112

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We design a sequential Monte Carlo scheme for the dual purpose of Bayesian inference and model selection. We consider the application context of urban mobility, where several modalities of transport and different measurement devices can be employed. Therefore, we address the joint problem of online tracking and detection of the current modality. For this purpose, we use interacting parallel particle filters, each one addressing a different model. They cooperate for providing a global estimator of the variable of interest and, at the same time, an approximation of the posterior density of each model given the data. The interaction occurs by a parsimonious distribution of the computational effort, with online adaptation for the number of particles of each filter according to the posterior probability of the corresponding model. The resulting scheme is simple and flexible. We have tested the novel technique in different numerical experiments with artificial and real data, which confirm the robustness of the proposed scheme.

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Section: Mapf For Time-varying Modelsmentioning

confidence: 99%

Cooperative parallel particle filters for online model selection and applications to urban mobility

Martino

Read

Elvira

et al. 2017

Digital Signal Processing

136

112

View full text Add to dashboard Cite

show abstract

“…Particle filters: Since (13) and (17) form a linear model with non-Gaussian noise, nonlinear filtering/smoothing techniques based on particle filtering ( [13], see also [1] for a relevant contribution) can also be applied. This solution means that a set of new particles are created at each time instant, corresponding to the possibility that δ(t) is nonzero, and these will then survive if supported by future measurement.…”

Section: State Estimates Based On a Stochastic Frameworkmentioning

confidence: 99%

Smoothed state estimates under abrupt changes using sum-of-norms regularization

2012

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The presence of abrupt changes, such as impulsive and load disturbances, commonly occur in applications, but make the state estimation problem considerably more difficult than in the standard setting with Gaussian process disturbance. Abrupt changes often introduce a jump in the state, and the problem is therefore readily and often treated by change detection techniques. In this paper, we take a different approach. The state smoothing problem for linear state space models is here formulated as a constrained least-squares problem with sum-of-norms regularization, a generalization of 1-regularization. This novel formulation can be seen as a convex relaxation of the well known generalized likelihood ratio method by Willsky and Jones. Another nice property of the suggested formulation is that it only has one tuning parameter, the regularization constant which is used to trade off fit and the number of jumps. Good practical choices of this parameter along with an extension to nonlinear state space models are given.

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“…A novel Bayesian estimator, called particle filter, overcomes the limitations of the Kalman filter in that it is generally applicable to nonlinear and non-Gaussian state-space models (Andrieu, Doucet et al 2004). The particle filter approximates the posterior distribution of estimates by a large number of samples (particles), each with a normalized assigned weight.…”

Section: State Estimation and Parameter Estimation Paradigmsmentioning

confidence: 99%

Internal models in sensorimotor integration: perspectives from adaptive control theory

2005

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Internal model and adaptive control are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning are reviewed and their possible relevance to motor control is discussed. Possible applicability of Luenberger observer and extended Kalman filter to state estimation problems such as sensorimotor prediction or the resolution of vestibular sensory ambiguity is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal model in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.

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Particle Methods for Change Detection, System Identification, and Control

Cited by 280 publications

References 38 publications

Cooperative parallel particle filters for online model selection and applications to urban mobility

Cooperative parallel particle filters for online model selection and applications to urban mobility

Smoothed state estimates under abrupt changes using sum-of-norms regularization

Internal models in sensorimotor integration: perspectives from adaptive control theory

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