Journal ArticleDOI
Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters
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TLDR
The MAPF is significantly more computationally efficient than a comparable particle filter that runs on the full augmented state and can handle sensor and actuator offsets as unknown means in the noise distributions, avoiding the standard approach of augmenting the state with such offsets.About:
This article is published in Automatica.The article was published on 2013-06-01. It has received 94 citations till now. The article focuses on the topics: Gaussian noise & Filtering problem.read more
Citations
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Journal ArticleDOI
Extended Target Tracking Using Gaussian Processes
Niklas Wahlström,Emre Ozkan +1 more
TL;DR: This paper proposes using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan, that creates a model that describes the shape and the kinematics of the object.
Journal ArticleDOI
Noise covariance matrices in state-space models: A survey and comparison of estimation methods—Part I
TL;DR: This paper deals with the estimation of the noise covariance matrices of systems described by state‐space models and a simulation comparison using exemplary MATLAB implementations of the methods is provided.
Journal ArticleDOI
Tire-Stiffness and Vehicle-State Estimation Based on Noise-Adaptive Particle Filtering
Karl Berntorp,Stefano Di Cairano +1 more
TL;DR: A Bayesian approach is formulated, in which particle filtering and the marginalization concept are used to estimate in a computationally efficient way the tire-stiffness parameters and the vehicle state using only wheel-speed and inertial sensors.
Journal ArticleDOI
A Robust Particle Filtering Algorithm With Non-Gaussian Measurement Noise Using Student-t Distribution
Dingjie Xu,Chen Shen,Feng Shen +2 more
TL;DR: This letter endow the unknown measurement noise with the Student-t distribution to model the underlying non-Gaussian dynamics of a real physical system and develops a robust particle filtering algorithm based on variational Bayesian approach.
Journal ArticleDOI
Robust Consensus Nonlinear Information Filter for Distributed Sensor Networks With Measurement Outliers
TL;DR: A robust consensus nonlinear information filter for distributed state estimation with measurement outliers with Gaussian approximation under the framework of the information filter is proposed.
References
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Journal ArticleDOI
Information Theory and Statistical Mechanics. II
TL;DR: In this article, the authors consider statistical mechanics as a form of statistical inference rather than as a physical theory, and show that the usual computational rules, starting with the determination of the partition function, are an immediate consequence of the maximum-entropy principle.
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Theory of probability
Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal ArticleDOI
Theory of Probability.
Ernest Nagel,Harold Jeffreys +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.
Journal Article
Prior Probabilities
TL;DR: It is shown that in many problems, including some of the most important in practice, this ambiguity can be removed by applying methods of group theoretical reasoning which have long been used in theoretical physics.