A convergence analysis of the modified unscented Kalman filter (UKF), used as an observer for a class nonlinear deterministic continuous time systems, is presented. Under certain conditions, extended (EKF) exponential non-linear i.e., dynamics estimation error exponentially stable. It shown that unlike EKF, UKF not converging observer. modification – proposed, which better candidate This paper ...

Probabilistic inference is the problem of estimating the hidden states of a system in an optimal and consistent fashion given a set of noisy or incomplete observations. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive online. This posterior density constitut...

Abstract Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS-INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange INS measurements real-time positioning, using conventional EKF FGO with a dataset collected in urban canyon Hong Kong. The strength is analyze...

In this paper, a new method, based on the estimation of irradiation and temperature values, was proposed for Maximum Power Point Tracking (MPPT) in photovoltaic systems. The method is Extended Kalman Particle Filter (EKPF). Given that basis particle filter, firstly, performed with high accuracy, although target system has severe nonlinearity; secondly, there no limitation probability density fu...

Simultaneous localization and mapping (SLAM) is a fundamental problem in robotics. It is the process of creating a map of the environment while, at the same time, estimating the position and attitude of the robot relative to the map. SLAM enables autonomous path planning and control. The extended Kalman Filter (EKF), Particle Filters, and Expectation Minimization are among the most popular SLAM...

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 ...

In this paper we present a finite dimensional iterative algorithm for optimal maximum a posteriori (MAP) state estimation of bilinear systems. Bilinear models are appealing in their ability to represent or approximate a broad class of nonlinear systems. We show that several bilinear models previously considered in the literature are special cases of the general bilinear model we propose. Our it...