A weighted likelihood criteria for learning importance densities in particle filtering

A weighted likelihood criteria for learning importance densities in particle filtering

Abstract Selecting an optimal importance density and ensuring optimal particle weights are central challenges in particle-based filtering. In this paper, we provide a two-step procedure to learn importance densities for particle-based filtering. The first stage importance density is constructed base...

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Bibliographic Details
Main Authors: Muhammad Javvad ur Rehman, Sarat Chandra Dass, Vijanth Sagayan Asirvadam
Format: Article
Language:English
Published: SpringerOpen 2018-06-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
Nonlinear state-space models
Particle filter
Ensemble Kalman filter
Gaussian mixture models
Expectation-maximization (EM) algorithm
Online Access:http://link.springer.com/article/10.1186/s13634-018-0557-5

Internet

http://link.springer.com/article/10.1186/s13634-018-0557-5

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