Abstract

This paper presents a recursive algorithm for the least-squares linear fixed-interval smoothing problem of discrete-time signals using randomly delayed measurements perturbed by an additive white noise. It is assumed that the autocovariance function of the signal is expressed in a semi-degenerate kernel form and the delay is modelled by a sequence of independent Bernoulli random variables, which indicate if the measurements are up-to-date or delayed by one sampling time. The estimators do not use the state-space model of the signal but only the covariance information about the signal and the additive noise in the observations and the delay probabilities.

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

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.