Publication Type:

Journal Article

Source:

Lecture Notes in Electrical Engineering(accepted for publication), Springer Verlag, Volume 521, p.91-97 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056338461&doi=10.1007%2f978-981-13-1906-8_10&partnerID=40&md5=48be739ae67541e64678023d3704f144

Keywords:

Equations of state, Error detection, Estimation, Extended particle filters, Interactive computer systems, Monte Carlo methods, Non-linear signal processing, Nonlinear and non-Gaussian, Nonlinear equations, Particle filter, Particle filter algorithms, Real time systems, Signal processing, State estimation, State space equation, State vector estimation, Surface discharges, Unscented particle filters

Abstract:

Many applications of engineering require the state estimation of the real-time systems. The real-time dynamic systems are normally modeled as discrete time state space equations. The behaviors of the state space equations of many of the dynamic systems are nonlinear and non-Gaussian. Particle filter is one of the methods used for the analysis of these dynamic systems. In this review paper, many modified variants of particle filter algorithms and its application to different dynamic systems are discussed. State vector estimation using modified variants of particle filter was discussed and compared with the other standard algorithms.

Notes:

cited By 0

Cite this Research Publication

P. Sudheesh and Jayakumar, M., “Nonlinear Signal Processing Applications of Variants of Particle Filter: A Survey”, Lecture Notes in Electrical Engineering(accepted for publication), vol. 521, pp. 91-97, 2017.