There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-ga...

There is a growing interest in using Kalman-filter models for brain modelling. In turn, it is of considerable importance to represent Kalman-filter in connectionist forms with local Hebbian learning rules. To our best knowledge, Kalman-filter has not been given such local representation. It seems that the main obstacle is the dynamic adaptation of the Kalman-gain. Here, a connectionist represen...

In the literature on filter design, the system whose state has to be estimated is usually assumed known. However, in most practical situations, this assumption does not hold, and a two-step procedure is adopted: 1) a model is identified from a set of noise-corrupted data; 2) on the basis of the identified model, a Kalman filter is designed. In this paper, the idea of directly identifying the fi...

This paper examines the recently developed p-shift iterative unbiased Kalman-like algorithm intended for filtering (p = 0), prediction (p > 0), and smoothing (p < 0) of linear discrete time-varying state-space models in non Gaussian environment with uncertainties. The algorithm is designed to have no requirements for noise and initial conditions and becomes optimal on large averaging intervals....

-Target tracking is one of the major aspects often used in sonar applications, surveillance systems, communication systems, embedded applications etc. To obtain kinematic components of a moving target such as position, velocity, and acceleration, one of the most used approaches in target tracking is stochastic estimation approach. Movement of the target is described by state space dynamic syste...

This paper presents a distributed receding horizon filtering algorithm for multisensor continuous-time linear stochastic systems. Distributed fusion with a weighted sum structure is applied to local receding horizon Kalman filters having different horizon lengths. The fusion estimate of the state of a dynamic system represents the optimal linear fusion by weighting matrices under the minimum me...

Data assimilation (DA) has been applied in an estuarine system in order to implement operational analysis in the management of a coastal zone. The dynamical evolution of the estuarine variables and corresponding observations are modelled with a nonlinear state-space model. Two DA methods are used for controlling the evolution of the model state by integrating information from observations. Thes...

According to the poor adaptive ability of traditional filter algorithm in the estimation for traffic state and travel time with Kalman filter, an improved fuzzy adaptive Kalman filtering method was proposed. The new interest of observation noise was defined, and the fuzzy logic was used to adjust the importance weights of system noise and observation noise through on-line monitoring the interes...

The nonlinear filters based on Taylor series approximation are broadly used for computational simplicity, even though their filtering estimates are clearly biased. In this paper, first, we analyze what is approximated when we apply the expanded nonlinear functions to the standard linear recursive Kalman filter algorithm. Next, since the state variables αt and αt−1 are approximated as a conditio...

This paper introduces a new Kalman filter-based method for detecting sensor faults in linear dynamic systems. In contrast with existing sequential fault-detection algorithms, the proposed method enables direct evaluation of the integrity risk, which is the probability that an undetected fault causes state estimate errors to exceed predefined bounds of acceptability. The new method is also compu...