Abstract
The development of particle filters for emerging data transmission applications is essential to address network-related issues. In this Letter, a robust particle filter algorithm for non-linear systems with one-step randomly delayed measurements and outlier corrupted measurement noise is investigated. To address the issue of measurement outliers, a Student's-t distribution is utilised to model the measurement noise. For the random delay uncertainties, a Bernoulli random variable is employed, based on which the likelihood function is transformed into an exponential multiplication form. The filter state estimation is obtained by Bayes's theorem and using variational Bayesian approach, the latent random variables and Bernoulli random variable are estimated together. The superior performance of the proposed method is verified through a typical example by comparison with existing particle filters that consider random measurement delay or heavy-tailed measurement noise.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call