In the previous papers (Masreliez and Martin, 1977; Novovičova, 1987; Schick and Mitter, 1994) the problem of recursive estimation of linear dynamic systems parameters and of the state of such systems in the presence of outliers in observations have been considered. In this connection various ordinary recursive techniques are worked out, when systems output is corrupted by an additive noise wit...

This paper proposes a localized recursive estimation scheme for parameter estimation in wireless sensor networks. Given any parameter of a target occurring at some location and time, a number of sensors recursively estimate the parameter by using their local measurements of the parameter that is attenuated with the distance between a sensor and the target location and corrupted by noise. Compar...

In the previous paper (Pupeikis, 1998), the problem of recursive estimation of the state of linear dynamic systems, described by an autoregressive model (AR), in the presence of timevarying outliers in observations to be processed has been considered. An approach to the robust recursive state estimation has been obtained and proved by estimating the real chemical process (Box and Jenkins, 1970)...

Recursive estimation deals with the problem of extracting information about parameters, or states, of a dynamical system in real time, given noisy measurements of the system output. Recursive estimation plays a central role in many applications of signal processing, system identification and automatic control. In this thesis we study nonlinear and non-Gaussian recursive estimation problems in d...

Recursive Estimation 3D motion estimate through optical flow fields is also called interface motion estimation”, since it only estimate the relative motion between two frames, to obtain absolute motion, a recursive motion estimation system was first used. In this system, the global motion can be given in the following way are the absolute motion at time instant and, respectively, the two-view m...

Recursive (online) expectation–maximization (EM) algorithm along with stochastic approximation is employed in this paper to estimate unknown time-invariant/variant parameters. The impulse response of a linear system (channel) is modeled as an unknown deterministic vector/process and as a Gaussian vector/process with unknown stochastic characteristics. Using these models which are embedded in wh...

A noise canceling technique based on a model-based recursive processor is presented. Beginning with a canceling scheme using a reference source, it is shown how to obtain an estimate of the noise, which can then be incorporated in a recursive noise canceler. Once this is done, recursive detection and estimation schemes are developed. The approach is to model the nonstationary noise as an autore...