Results 1 to 10 of about 7,235 (128)
Iterative Truncated Unscented Particle Filter [PDF]
Information, 2020 The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems.
Yanbo Wang +3 more
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Self-Driving Car Location Estimation Based on a Particle-Aided Unscented Kalman Filter
Sensors, 2020 Localization is one of the key components in the operation of self-driving cars. Owing to the noisy global positioning system (GPS) signal and multipath routing in urban environments, a novel, practical approach is needed.
Ming Lin, Jaewoo Yoon, Byeongwoo Kim
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Sensors, 2018 In this paper, a novel robust particle filter is proposed to address the measurement outliers occurring in the multiple autonomous underwater vehicles (AUVs) based cooperative navigation (CN).
Ying Fan +4 more
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Научный вестник МГТУ ГА, 2018 The paper describes two modified implementations of unscented Kalman filter (UKF) and unscented particle filter (UPF) to solve nonlinear filtering problem for discrete-time dynamic space model (DSSM).
I. A. Kudryavtseva, M. V. Lebedev
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A Localization Based on Unscented Kalman Filter and Particle Filter Localization Algorithms [PDF]
IEEE Access, 2020 Localization plays an important role in the field of Wireless Sensor Networks (WSNs) and robotics. Currently, localization is a very vibrant scientific research field with many potential applications.
Inam Ullah +4 more
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Robust information unscented particle filter based on M‐estimate
IET Signal Processing, 2019 Herein, inspired by the unscented particle filter (UPF), an improved particle filter (PF) using an unscented information filter based on M‐estimate (UIF‐BM) to approximate the importance density function (IDF) is designed. The filtering method is proposed to obtain an accurate state estimation for a non‐linear discrete time dynamical system with non ...
Shenmin Song
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Unscented Kalman Filters and Particle Filter Methods for Nonlinear State Estimation
Procedia Technology, 2014 AbstractFor nonlinear state space models to resolve the state estimation problem is difficult or these problems usually do not admit analytic solution. The Extended Kalman Filter (EKF) algorithm is the widely used method for solving nonlinear state estimation applications.
Katalin György, László David
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Научный вестник МГТУ ГА, 2016 The paper contains algorithms for solving the problem of nonlinear filtering. The nonlinear approximate filters presented are: the extended Kalman filter (EKF), the uncented Kalman filter (UKF) and unscented Particle Filter (UPF). The flow-charts for the
I. A. Kudryavtseva
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Wrapped Particle Filtering for Angular Data
IEEE Access, 2022 Particle filtering is probably the most widely accepted methodology for general nonlinear filtering applications. The performance of a particle filter critically depends on the choice of proposal distribution.
Guddu Kumar +4 more
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International Journal of Electrochemical Science, 2020 Accurate estimation of State of Charge (SOC) of wireless sensor network nodes is of great significance for wireless sensor network layout. A combination strategy method based on unscented particle filter using weight particle swarm optimization (PSO-UPF)
Lei Chen +4 more
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