Abstract
This paper addresses the signal estimation problem in situations where the observations are nonlinear functions of the signal and the measure mechanism is prone to failure or some observations are accidentally lost (uncertain observations). A recursive filtering and fixed-point smoothing algorithm is proposed assuming that the Bernoulli random variables describing the uncertainty in the observations are independent and the state-space model generating the signal is unknown and only the covariance functions of the processes involved in the observation equation are available. A numerical simulation example concerning the phase modulation problem shows the effectiveness of the proposed algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.