— This paper presents review of techniques and algorithms used for filtering and data association in visual tracking. Kalman filter is an optimal Bayesian filter for linear dynamic models with Gaussian noise. Most of the processes and systems in real world are nonlinear, and in these situations there is extension of Kalman filter named the Extended Kalman filter (EKF). In case when the noise is...

Dual estimation refers to the problem of simultaneously estimating the state of a dynamic system and the model which gives rise to the dynamics. Algorithms include expectation-maximization (EM), dual Kalman filtering, and joint Kalman methods. These methods have recently been explored in the context of nonlinear modeling, where a neural network is used as the functional form of the unknown mode...

Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based l...

The problem of nonlinear estimation is reexamined, and a new semi-parametric representation of uncertainty called the Biscay distribution is presented. The Biscay distribution is combined with the extended Kalman filter (EKF) and a new filtering paradigm called the Biscay distribution filter (BDF) is developed. The BDF is provably optimal for linear estimation and generalizes naturally to nonli...

Robot localization using odometry and feature measurements is a nonlinear estimation problem. An efficient solution is found using the extended Kalman filter, EKF. The EKF however suffers from divergence and inconsistency when the nonlinearities are significant. We recently developed a new type of filter based on an auxiliary variable Gaussian distribution which we call the antiparticle filter ...

A new filtering algorithm, adaptive square root cubature Kalman filter-Kalman filter (SRCKF-KF) is proposed to reduce the problems of amount of calculation, complex formula-transform, low accuracy, poor convergence or even divergence. The method uses cubature Kalman filter (CKF) to estimate the nonlinear states of model while its linear states are estimated by the Kalman filter (KF). The simula...

Extended Kalman Filter (EKF) algorithm is widely used in GPS positioning and velocity measurement. As for EKF algorithm, the approximate initial position of the receiver is indispensable; otherwise the time consumption of the first positioning is too high because of the filter’s low convergence rate. A modified EKF algorithm named delayed update EKF (DU-EKF) algorithm for GPS point dynamic posi...

Abstract Quadrotor UAV obtain the attitude calculation result from inertial measurement unit (IMU), but IMU has problems of high noise and low precision, to solve problems, this paper first introduces extended Kalman filter (EKF) complementary filter, uses them output accelerometer magnetic sensor. The effects two methods are compared by static testing dynamic testing. results show that both fi...

This paper describes a method for nonlinear filtering based on an adaptive observer, which guarantees the local stability of the linearized error system. A fake algebraic Riccati equation is employed in the calculation of the filter gain. The design procedure attempts to produce a stable filter at the expense of optimality. This contrasts with the extended Kalman filter (EKF), which attempts to...

In this paper, an improved nonlinear Gaussian mixture probability hypothesis density (GM-PHD) filter is proposed to address bearings-only measurements in multi-target tracking. The proposed method, called the Gaussian mixture measurements-probability hypothesis density (GMM-PHD) filter, not only approximates the posterior intensity using a Gaussian mixture, but also models the likelihood functi...