The classical recursive three‐step filter can be used to estimate the state and unknown input
when the system is affected by unknown input, but the recursive three‐step filter cannot be …

A method is developed for the state estimation of linear time-varying discrete systems with
unknown inputs. By making use of the two-stage Kalman filtering technique and a proposed …

The strong tracking filter (STF) proposed by Zhou et al. in 1992, which was developed for
nonlinear systems with white noise, is extended to a class of nonlinear time-varying …

The two-stage Kalman estimator was originally proposed to reduce the computational
complexity of the augmented state Kalman filter. It was also applied to the tracking of …

An exact solution is provided for the multiple sensor bias estimation problem based on local
tracks. It is shown that the sensor bias estimates can be obtained dynamically using the …

A novel solution is provided for the bias estimation problem in multiple asynchronous
sensors using common targets of opportunity. The decoupling between the target state …

The paper studies the problem of simultaneously estimating the state and the fault of linear
stochastic discrete-time varying systems with unknown inputs. The fault and the unknown …

The two-stage alpha-beta-gamma estimator is proposed as an alternative to adaptive gain
versions of the alpha, beta and alpha, beta, and gamma filters for tracking maneuvering …

This paper studies the problem of multi-sensor multi-target tracking with registration errors in
the formulation of random finite sets. The probability hypothesis density (PHD) recursion is …

In this paper, multisensor-multitarget tracking performance with bias estimation and
compensation is investigated when only moving targets of opportunity are available. First …