<span lang="EN-US">This paper presents the comparison between optimized unscented Kalman filter (UKF) and extended (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF EKF depends on accurate selection state noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find optimal values main o...

Acoustic travel-time tomography of the atmosphere is a nonlinear inverse problem which attempts to reconstruct temperature and wind velocity fields in the atmospheric surface layer using the dependence of sound speed on temperature and wind velocity fields along the propagation path. This paper presents a statistical-based acoustic travel-time tomography algorithm based on dual state-parameter ...

Some applications are constrained only to implement low cost receivers. In this case, designers are required to use less complex and non-expensive modulation techniques. Differential Quadrature Phase Shift Keying (DQPSK) and Gaussian Frequency Shift Keying (GFSK) can be non-coherently demodulated with simple algorithms. However, these types of demodulation are not robust and suffer from poor pe...

the radar tracking is one of the best leo satellite tracking methods. while the tracking filters which are mostly linear, and them are not able to have a precise estimation of the objects with nonlinear motion dynamic such as satellite, we should use nonlinear filters. in this paper , firstly, we deal with the problem of the leo satellites motion path modeling according to the satellite motion ...

This paper develops a Square Root Unscented Kalman Filter (SRUKF) for performing video-rate visual simultaneous localization and mapping (SLAM) using a single camera. The conventional UKF has been proposed previously for SLAM, improving the handling of non-linearities compared with the more widely-used Extended Kalman Filter. However, no account was taken of the comparative complexity of the al...

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no apriori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion ( pixels) using multiple filter bank in an IR image sequ...

One of the major challenges in human-robot interaction is how to enable the use of unrestricted hand motion as a tool for communication. The direct use of hand as an input tool enables the user to connect to systems more naturally, allowing them to become an integral part of our daily lives. A vision-based approach, using cameras to capture data, supports non-contact and unrestricted movement o...

Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed....

This paper presents a new hybrid methodology for learning Sugeno-type fuzzy models via subtractive clustering, Adaptive Boosting Regression (AdaBoostR) and Unscented Kalman Filter (UKF). The generated fuzzy models are used for modeling nonlinear benchmark processes. In the proposed procedure, first one fuzzy rule is generated by subtractive clustering algorithm from available data of a given no...

Different approaches for the estimation of the states of linear dynamic systems are commonly used, the most common being the Kalman filter. For nonlinear systems, variants of the Kalman filter are used. Some of these variants include the LKF (linearized Kalman filter), the EKF (extended Kalman filter), and the UKF (unscented Kalman filter). With the LKF and EKF, performance varies depending on ...