Pearson type VII distribution‐based robust Kalman filter under outliers interference. Issue 8 (3rd June 2019)

• Record Type: Journal Article
• Authors: Peng, Yun; Pan‐long, Wu; Shan, He
• Is Part Of: IET radar, sonar & navigation. Volume 13:Issue 8(2019)
• Page Start: 1389
• Page End: 1399
• Publication Date: 2019-06-03
• Subjects: Bayes methods -- target tracking -- state estimation -- statistical distributions -- discrete time filters; Pearson type VII distribution‐based robust Kalman filter -- state estimation -- linear discrete time system -- variational Bayesian method -- judgement factor -- beta‐Bernoulli distribution -- measurement modelling -- outlier noise -- PTV distribution -- VB method -- outlier interference -- target tracking; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2018.5534
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : In this study, the authors focus on state estimation in a linear discrete time system where measurements are uncertain due to possible outliers. A novel robust Kalman filter based on the Pearson type VII (PTV) distribution is proposed by using the variational Bayesian (VB) method and tested in a target tracking example. To judge whether the measurement is an outlier, a judgement factor that follows the beta‐Bernoulli distribution is introduced in the measurement modelling. In addition, the outlier noise is approximated by the PTV distribution considering the heavy tail characteristic and it is jointly estimated with the judgement factor and state using the VB method. The simulation results show that the proposed filter has better estimation accuracy than most existing robust filters.