Comparison of Kalman filter and particle filter used for localization of an underwater vehicle

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

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Tracking of pendulum using particle filter with residual resampling

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.10246 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 12

Author(s):

Penumarty Hiranmayi ◽

Kola Sai Gowtham ◽

S Koteswara Rao ◽

V Gopi Tilak

Keyword(s):

Monte Carlo ◽

Kalman Filter ◽

Particle Filter ◽

Extended Kalman Filter ◽

Horizontal Direction ◽

Bayesian Filtering ◽

Discrete State ◽

Harmonic Motion ◽

Simple Pendulum ◽

Importance Resampling

The phenomenon of simple harmonic motion is more vigilantly explained using a simple pendulum. The angular motion of a pendulum is linear in nature. But the analysis of the motion along the horizontal direction is non-linear. To estimate this, several algorithms like the Kalman filter, Extended Kalman Filter etc. are adopted. Here in this paper, Particle filter is chosen which is a method to form Monte Carlo approximations to the solutions of Bayesian filtering equations. Sequential importance resampling based Particle filters are used where the filtering distributions are multi-nodal or consist of discrete state components since under these circumstances the Bayesian approximations do not always work well.

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An Implicit Algorithm of Solving Nonlinear Filtering Problems

Communications in Computational Physics ◽

10.4208/cicp.180313.130214a ◽

2014 ◽

Vol 16 (2) ◽

pp. 382-402

Author(s):

Feng Bao ◽

Yanzhao Cao ◽

Xiaoying Han

Keyword(s):

Kalman Filter ◽

Particle Filter ◽

Nonlinear Filtering ◽

State Equation ◽

Nonlinear Filter ◽

Filter Method ◽

Simulation Methods ◽

Current State ◽

Numerical Simulation Methods ◽

Kalman Filter Method

AbstractNonlinear filter problems arise in many applications such as communications and signal processing. Commonly used numerical simulation methods include Kalman filter method, particle filter method, etc. In this paper a novel numerical algorithm is constructed based on samples of the current state obtained by solving the state equation implicitly. Numerical experiments demonstrate that our algorithm is more accurate than the Kalman filter and more stable than the particle filter.

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Use of Kalman Filter and Particle Filter in a One Dimensional Leachate Transport Model

Proceedings of the 2007 National Conference on Environmental Science and Technology ◽

10.1007/978-0-387-88483-7_21 ◽

2009 ◽

pp. 157-163

Author(s):

Shoou-Yuh Chang ◽

Sikdar M.I. Latif

Keyword(s):

Kalman Filter ◽

Particle Filter ◽

Transport Model ◽

One Dimensional

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A New Linear Regression Kalman Filter with Symmetric Samples

Symmetry ◽

10.3390/sym13112139 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2139

Author(s):

Xiuqiong Chen ◽

Jiayi Kang ◽

Mina Teicher ◽

Stephen S.-T. Yau

Keyword(s):

Kalman Filter ◽

Linear Regression ◽

Particle Filter ◽

Normal Distribution ◽

Extended Kalman Filter ◽

Nonlinear Filtering ◽

Unscented Kalman Filter ◽

Standard Normal Distribution ◽

Leibler Divergence ◽

Standard Normal

Nonlinear filtering is of great significance in industries. In this work, we develop a new linear regression Kalman filter for discrete nonlinear filtering problems. Under the framework of linear regression Kalman filter, the key step is minimizing the Kullback–Leibler divergence between standard normal distribution and its Dirac mixture approximation formed by symmetric samples so that we can obtain a set of samples which can capture the information of reference density. The samples representing the conditional densities evolve in a deterministic way, and therefore we need less samples compared with particle filter, as there is less variance in our method. The numerical results show that the new algorithm is more efficient compared with the widely used extended Kalman filter, unscented Kalman filter and particle filter.

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