Extended Kalman Filtering (EKF) is a nonlinear dynamic data reconciliation (NDDR) method. One of its main advantages is its suitability for on-line applications. This paper presents an on-line NDDR method using EKF. It is implemented for two case studies, temperature measurements of a distillation column and concentration measurements of a CSTR. In each time step, random numbers with zero m...

GPS/INS integration with uncertain dynamics modelling and uncertain measurement noise is studied. An extended interval Kalman filter is applied to such an uncertain integrated system, since the traditional extended Kalman filter is no longer applicable. The results of a computer simulation indicate that the extended Kalman filtering algorithm is consistent with the traditional extended Kalman f...

State estimation algorithm deals with recovering some desired state variables of a dynamic system from available noisy measurements, and estimation of the state variables is one of the fundamental and significant problems in control and signal processing areas, and many significant progresses have been made in this area. In 1940s, Wiener, the founder of the modern statistical estimation theory,...

A carrier synchronization algorithm for high-order QAM modulated signals that uses Kalman filtering techniques shows fast acquisition times and good frequency tracking performances. The algorithm relies on a decision directed extended Kalman filter combined with a lock detector which determines the status of the synchronization process, modifying consequently the filter parameters. Simulations ...

One of the disadvantages of passive-BD navigation system is that the pseudo-range error from its receiver is significant and difficult to be modeled. Therefore, passive-BD/SINS integrated navigation system using traditional indirect Kalman filtering method can not achieve high positioning accuracy and costs too much calculating time. This study optimizes the filtering algorithm for integrated s...

In this study, we propose an extended Kalman filter approach for the estimation of the human head tissue conductivities in vivo by using electroencephalogram (EEG) data. Since the relationship between the surface potentials and conductivity distribution is nonlinear, the proposed algorithm first linearizes the system and applies extended Kalman filtering. By using a three-compartment realistic ...

Classical extended Kalman filter algorithm is often used to obtain dynamic estimation of nodes’ position in wireless localization. However, it is prone to generate error accumulation in the filtering process, and lead to filter divergence, which causes low accuracy. The paper explores a strong tracking extended Kalman filter with algorithm a fading factor, which can adjust the gain K in real ti...

A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and ...

This paper presents a nonlinear approach for data reconciliation. The advantages of this approach over the conventional extended Kalman filtering (EKF) are namely, less linearization errors are generated in the process, and secondly the choice of the objective function is flexible. In this work, two probability density functions, namely the Logistic and Lorentz distribution are proposed as the ...