Unscented filtering algorithm using two-step randomly delayed observations in nonlinear systems. (English) Zbl 1185.93133
Summary: An unscented filtering algorithm is derived for a class of nonlinear discrete-time stochastic systems using noisy observations which can be randomly delayed by one or two sample times. The update and the possible delays (of one and two sampling times) of any observation are modelled by using three Bernoulli random variables such that only one of them takes the value one. The algorithm performs in two-steps, prediction and update, and it uses a scaled unscented transformation to approximate the conditional mean and covariance of the state and observation at each time. The performance of the proposed filter is shown in a simulation example which uses a growth model with randomly delayed observations; in this example, the proposed filter is compared with the extended one obtained by linearizing the state and the observation equations and, also, with the unscented Kalman filter. A clear superiority of the proposed filter over the others is inferred.
MSC:
| 93E11 | Filtering in stochastic control theory |
| 62M20 | Inference from stochastic processes and prediction |
Keywords:
least squares estimation; nonlinear stochastic systems; randomly delayed observations; extended Kalman filter; unscented Kalman filterReferences:
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