Nonlinear Estimation of Discrete-Time Signals Under Random Observation Delay
- Departamento de Estadistica e I. O., Universidad de Jaen, Paraje Las Lagunillas, s/n, 23071 Jaen (Spain)
- Departamento de Estadistica e I. O., Universidad de Granada, Campus Fuentenueva, 18071 Granada (Spain)
- Department of Technology, Kagoshima University, 1-20-6, Kohrimoto, Kagoshima, 890-0065 (Japan)
This paper presents an approximation to the nonlinear least-squares estimation problem of discrete-time stochastic signals using nonlinear observations with additive white noise which can be randomly delayed by one sampling time. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values, zero or one, indicate that the real observation arrives on time or it is delayed and, hence, the available measurement to estimate the signal is not up-to-date. Assuming that the state-space model generating the signal is unknown and only the covariance functions of the processes involved in the observation equation are ready for use, a filtering algorithm based on linear approximations of the real observations is proposed.
- OSTI ID:
- 21251775
- Journal Information:
- AIP Conference Proceedings, Journal Name: AIP Conference Proceedings Journal Issue: 1 Vol. 1060; ISSN 0094-243X; ISSN APCPCS
- Country of Publication:
- United States
- Language:
- English
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