The interacting multiple model algorithm for systems with Markovian switching coefficients

Blom, H.A.P.; Bar‐Shalom, Yaakov

doi:10.1109/9.1299

Cited by 2,050 publications

(944 citation statements)

References 11 publications

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“…The state estimation problem addressed by [1], [2] among others, is to estimate the current hybrid state given all observations up to the current time step. These approaches build on early filtering work by [22]. We extend the work of [2] in order to deter-…”

Section: B Hybrid State Distribution 1) Exact Hybrid State Estimationmentioning

confidence: 99%

“…The key idea is to perform a forward-backward, or 'smoothing', Kalman Filter recursion for each possible mode sequence [23]: as noted by [22], in a non-real time approach, smoothing is preferable to a filtering algorithm such as [22]. This recursion deter- , θ), the posterior probability of the discrete mode sequence using Bayes' Rule:…”

Section: B Hybrid State Distribution 1) Exact Hybrid State Estimationmentioning

confidence: 99%

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Model learning for switching linear systems with autonomous mode transitions

Blackmore

Gil

Chung

et al. 2007

2007 46th IEEE Conference on Decision and Control

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Abstract-We present a novel method for model learning in hybrid discrete-continuous systems. The approach uses approximate Expectation-Maximization to learn the MaximumLikelihood parameters of a switching linear system. The approach extends previous work by 1) considering autonomous mode transitions, where the discrete transitions are conditioned on the continuous state, and 2) learning the effects of control inputs on the system. We evaluate the approach in simulation.

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Section: B Hybrid State Distribution 1) Exact Hybrid State Estimationmentioning

confidence: 99%

Section: B Hybrid State Distribution 1) Exact Hybrid State Estimationmentioning

confidence: 99%

Model learning for switching linear systems with autonomous mode transitions

Blackmore

Gil

Chung

et al. 2007

2007 46th IEEE Conference on Decision and Control

View full text Add to dashboard Cite

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“…An important technique for managing multiple interacting dynamic models relies on the Markovian switching systems, also known in general as Interacting Multiple Model (IMM) [13]. Markovian switching systems model a process that changes in time by providing different models for each of the underlying processes.…”

Section: From Saliency Map To Gaze Predictionmentioning

confidence: 99%

From Saliency to Eye Gaze: Embodied Visual Selection for a Pan-Tilt-Based Robotic Head

Mancaş

Pirri

Pizzoli

2011

Advances in Visual Computing

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Abstract. This paper introduces a model of gaze behavior suitable for robotic active vision. Built upon a saliency map taking into account motion saliency, the presented model estimates the dynamics of different eye movements, allowing to switch from fixational movements, to saccades and to smooth pursuit. We investigate the effect of the embodiment of attentive visual selection in a pan-tilt camera system. The constrained physical system is unable to follow the important fluctuations characterizing the maxima of a saliency map and a strategy is required to dynamically select what is worth attending and the behavior, fixation or target pursuing, to adopt. The main contributions of this work are a novel approach toward real time, motion-based saliency computation in video sequences, a dynamic model for gaze prediction from the saliency map, and the embodiment of the modeled dynamics to control active visual sensing.

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“…In these fields, the problems of state estimation for MJLSs play an essential role in recovering some desired variables from given noisy observations for output variables. However, many approaches of achieving the state estimation of MJLSs include the generalized pseudoBayesian (GPB) algorithm [4,5], the interacting multiple model (IMM) filtering [6], stochastic sampling based methods [7,8], and LMMSE filter. Those methods are different from each other in their estimation criteria and means [2,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Optimal State Estimation for Discrete-Time Markov Jump Systems with Missing Observations

Sun

Zhao

Yin

2014

Abstract and Applied Analysis

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This paper is concerned with the optimal linear estimation for a class of direct-time Markov jump systems with missing observations. An observer-based approach of fault detection and isolation (FDI) is investigated as a detection mechanic of fault case. For systems with known information, a conditional prediction of observations is applied and fault observations are replaced and isolated; then, an FDI linear minimum mean square error estimation (LMMSE) can be developed by comprehensive utilizing of the correct information offered by systems. A recursive equation of filtering based on the geometric arguments can be obtained. Meanwhile, a stability of the state estimator will be guaranteed under appropriate assumption.

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The interacting multiple model algorithm for systems with Markovian switching coefficients

Cited by 2,050 publications

References 11 publications

Model learning for switching linear systems with autonomous mode transitions

Model learning for switching linear systems with autonomous mode transitions

From Saliency to Eye Gaze: Embodied Visual Selection for a Pan-Tilt-Based Robotic Head

Optimal State Estimation for Discrete-Time Markov Jump Systems with Missing Observations

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