Abstract
The optimal filtering problem is investigated for a class of discrete stochastic systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements. The random disturbances existing in the system are characterized by the multiplicative noises and the phenomena of sensor delay and missing measurements occur in a random way. The random sensor delay and missing measurements are described by two Bernoulli distributed random variables with known conditional probabilities. By using the state augmentation approach, the original system is converted into a new discrete system where the random one-step sensor delay and missing measurements exist in the sensor output. The new process noises and observation noises consist of the original stochastic terms, and the process noises are still autocorrelated. Then, based on the minimum mean square error (MMSE) principle, a new linear optimal filter is designed such that, for the finite-step autocorrelated process noises, random one-step sensor delay and missing measurements, the estimation error is minimized. By solving the recursive matrix equation, the filter gain is designed. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed filtering scheme.
Published Version
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 callMore From: Communications in Nonlinear Science and Numerical Simulation
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.
