Likelihood function modeling of particle filter in presence of non-stationary non-Gaussian measurement noise

DOI10.1016/j.sigpro.2009.12.005zbMath1197.94095OpenAlexW1969149220MaRDI QIDQ1957761

Arpita Mukherjee, Aparajita Sengupta

Publication date: 27 September 2010

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2009.12.005

zbMATH Keywords

likelihood functionparticle filterGaussian mixture model

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (2)

Active fault diagnosis of 2 DoF helicopter using particle filter-based log-likelihood ratio ⋮ Quadratic covariance‐constrained filtering for linear and non‐linear systems with non‐Gaussian noises

Cites Work

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• Sequential Monte Carlo Methods in Practice
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• Adaptive estimation in linear systems with unknown Markovian noise statistics
• Mixture Kalman Filters
• Gaussian sum particle filtering
• Nonlinear Bayesian estimation using Gaussian sum approximations

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