-Target tracking is one of the major aspects often used in sonar applications, surveillance systems, communication systems, embedded applications etc. To obtain kinematic components of a moving target such as position, velocity, and acceleration, one of the most used approaches in target tracking is stochastic estimation approach. Movement of the target is described by state space dynamic syste...

It is an important task to reliably detect and track multiple moving objects for video surveillance and monitoring. However, when occlusion occurs in nonlinear motion scenarios, many existing methods often fail to continuously track multiple moving objects of interest. In this paper we propose an effective approach for detection and tracking of multiple moving objects with occlusion. Moving tar...

[1] Many studies have shown the deficiencies of the extended Kalman filter (EKF), even though it has become a standard technique used in nonlinear estimation. In the EKF method, the state distribution is propagated analytically through the first-order linearization of the nonlinear system, which can introduce large errors in variable estimation and may lead to suboptimal performance and sometim...

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 ...

A new adaptive Unscented Kalman Filter (UKF) algorithm for actuator failure estimation is proposed. A novel filter method with the ability to adapt to the statistical characteristics of noise is presented to improve the estimation accuracy of traditional UKFs. A new algorithm (Kalman Filter (KF) ‐based adaptive UKF), with the ability to adapt to the statistical characterist...

Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model–unscented Kalman filter (IMM-UKF) algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variabl...

Relative navigation is a challenging technological component of many planned NASA and ESA missions. It typically uses recursive filters to fuse measurements (e.g., range and angle) from sensors with contrasting accuracies to estimate the vehicle state vectors in real time. The tendency of Extended Kalman filter to diverge under these conditions is well documented in the literature. As such, we ...