Machine learning meets Kalman Filtering

Carron, Andrea; Todescato, Marco; Carli, Ruggero; Schenato, Luca; Pillonetto, Gianluigi

doi:10.1109/cdc.2016.7798968

Cited by 32 publications

(24 citation statements)

References 11 publications

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“…The Kalman filter (KF) was proposed as a novel approach for the state estimation of dynamic linear state system in [ 21 ], and it has been widely implemented in various applications [ 22 – 29 ].…”

Section: Methodsmentioning

confidence: 99%

A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images

Chen

Miaou

2017

Journal of Healthcare Engineering

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Noninvasive medical procedures are usually preferable to their invasive counterparts in the medical community. Anemia examining through the palpebral conjunctiva is a convenient noninvasive procedure. The procedure can be automated to reduce the medical cost. We propose an anemia examining approach by using a Kalman filter (KF) and a regression method. The traditional KF is often used in time-dependent applications. Here, we modified the traditional KF for the time-independent data in medical applications. We simply compute the mean value of the red component of the palpebral conjunctiva image as our recognition feature and use a penalty regression algorithm to find a nonlinear curve that best fits the data of feature values and the corresponding levels of hemoglobin (Hb) concentration. To evaluate the proposed approach and several relevant approaches, we propose a risk evaluation scheme, where the entire Hb spectrum is divided into high-risk, low-risk, and doubtful intervals for anemia. The doubtful interval contains the Hb threshold, say 11 g/dL, separating anemia and nonanemia. A suspect sample is the sample falling in the doubtful interval. For the anemia screening purpose, we would like to have as less suspect samples as possible. The experimental results show that the modified KF reduces the number of suspect samples significantly for all the approaches considered here.

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Section: Methodsmentioning

confidence: 99%

A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images

Chen

Miaou

2017

Journal of Healthcare Engineering

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“…GPs have been widely used in the literature for modelling spatial and spatiotemporal processes, as well as for learning models of dynamical systems. A periodic sampling approach on a discretized grid over the environment is proposed in [8], in which the authors explore links between GPs and Kalman filters. Instead of sampling on a fixed grid, [9] proposes a sequential sampling approach for an agent navigating in the environment.…”

Section: Related Workmentioning

confidence: 99%

Risk-Aware Motion Planning in Partially Known Environments

Barbosa¹,

Lacerda²,

Duckworth³

et al. 2021

Preprint

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Recent trends envisage robots being deployed in areas deemed dangerous to humans, such as buildings with gas and radiation leaks. In such situations, the model of the underlying hazardous process might be unknown to the agent a priori, giving rise to the problem of planning for safe behaviour in partially known environments. We employ Gaussian process regression to create a probabilistic model of the hazardous process from local noisy samples. The result of this regression is then used by a risk metric, such as the Conditional Valueat-Risk, to reason about the safety at a certain state. The outcome is a risk function that can be employed in optimal motion planning problems. We demonstrate the use of the proposed function in two approaches. First is a sampling-based motion planning algorithm with an event-based trigger for online replanning. Second is an adaptation to the incremental Gaussian Process motion planner (iGPMP2), allowing it to quickly react and adapt to the environment. Both algorithms are evaluated in representative simulation scenarios, where they demonstrate the ability of avoiding high-risk areas.

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“…In this section, we briefly review the procedure for converting stationary GPs to state-space models. For a more detailed treatment, the reader is referred to [6][7][8].…”

Section: Conversion Of Gaussian Processesmentioning

confidence: 99%

“…However, it has recently been shown that stationary, temporal and spatiotemporal GPs can be transformed into equivalent (infinite dimensional) linear state-space systems by decomposing the GP's spectral density. This can subsequently be used together with Kalman filtering and Rauch-Tung-Striebel smoothing [6][7][8], which greatly alleviates the computational burden.…”

Section: Introductionmentioning

confidence: 99%

Rao-Blackwellized particle mcmc for parameter estimation in spatio-temporal Gaussian processes

Hostettler

Särkkä

Godsill

2017

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)

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In this paper, we consider parameter estimation in latent, spatiotemporal Gaussian processes using particle Markov chain Monte Carlo methods. In particular, we use spectral decomposition of the covariance function to obtain a high-dimensional state-space representation of the Gaussian processes, which is assumed to be observed through a nonlinear non-Gaussian likelihood. We develop a Rao-Blackwellized particle Gibbs sampler to sample the state trajectory and show how to sample the hyperparameters and possible parameters in the likelihood. The proposed method is evaluated on a spatio-temporal population model and the predictive performance is evaluated using leave-one-out cross-validation.

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Machine learning meets Kalman Filtering

Cited by 32 publications

References 11 publications

A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images

A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images

Risk-Aware Motion Planning in Partially Known Environments

Rao-Blackwellized particle mcmc for parameter estimation in spatio-temporal Gaussian processes

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