In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors. We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry problem. We present a detailed study of EKF-based VIO algorithms, by comparing both their theoretical properties and empirical performance. We show that an EKF formulation wh...

Obstacle avoidance is an essential function for the navigation of mobile robots. Noise filtering improves the measurement accuracy of senors and plays an important role for obstacle avoidance in the applications of mobile robots. This study evaluates the performance of the extended Kalman filtering (EKF) and Kalman filtering (KF) for obstacle avoidance of a two-wheeled mobile robot. EKF is an a...

The unmanned aerial vehicle (UAV) and the ground (UGV) can complete complex tasks through information sharing ensure mission execution capability of multiple carrier platforms. At same time, cooperative navigation use interaction between multi-platform sensors to improve relative positioning accuracy entire system. Aiming at problem deviation system model due gross errors in sensor measurement ...

Aiming at the problem that extended Kalman filter (EKF) is difficult to determine appropriate system and measurement noise matrices in parameter identification of direct-drive permanent magnet synchronous generator (D-PMSG), a method D-PMSG based on mean particle swarm optimization with extreme disturbance (EDMPSO)-EKF proposed. Firstly, by analysing principle EKF, dual-thread model established...

Accurate structural response prediction forms a main driver for health monitoring and control applications. This often requires the proposed model to adequately capture underlying dynamics of complex systems. In this work, we utilize learnable extended Kalman filter (EKF), named neural (neural EKF) throughout article, learning latent evolution physical The EKF is generalized version conventiona...

This paper proposes a robust algorithm for adaptive modelling of EEG signal classification using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis functions (RBF) and Autoregressive (AR) modeling and obtains better classification performance by truncating the filtering distribution when new observations are very informative.

Nonlinear estimation based on probabilistic inference forms a core component in most modern GNC systems. The estimator optimally fuses observations from multiple sensors with predictions from a nonlinear dynamic state-space model of the system under control. The current industry standard and most widely used algorithm for this purpose is the extended Kalman filter (EKF). Unfortunately, the EKF ...

The extended Kalman filter (EKF) and unscented Kalman filter (UKF) for nonlinear state estimation with both additive and nonadditive noise structures are presented and compared. Three different Global Positioning System (GPS)/inertial navigation system (INS) sensor fusion formulations for attitude estimation are used as case studies for the nonlinear state estimation problem. A diverse set of a...

One of the most important problem in target tracking is Line Of Sight (LOS) rate estimation for using from PN (proportional navigation) guidance law. This paper deals on estimation of position and LOS rates of target with respect to the pursuer from available noisy RF seeker and tracker measurements. Due to many important for exact estimation on tracking problems must target position and Line O...

To avoid damaging the motor and its surrounding equipment, detecting Inter-Turn Short Circuit (ITSC) faults in Permanent Magnet Synchronous Motors (PMSMs) applied electric vehicles is a crucial task. In this paper, detection of ITSC stator winding for PMSMs carried out by means Extended KALMAN Filter (EKF) algorithm combined with Fuzzy Logic Estimator (FLE). estimate parameters, including rotor...