Add to ORKGNonlinear filtering is certainly very important in estimation since most real-world problems are nonlinear. Recently a considerable progress in the nonlinear filtering theory has been made in the area of the sampling-based methods, including both random (Monte Carlo) and deterministic (quasi-Monte Carlo) sampling, and their combination. This work considers the problem of tracking a maneuvering target in a multisensor environment. A novel scheme for distributed tracking is employed that utilizes a nonlinear target model and estimates from local (sensor-based) estimators. The resulting estimation problem is highly nonlinear and thus quite challenging. In order to evaluate the performance capabilities of the architecture considered, ad...
In this paper we study the problem of tracking maneuvering targets in the framework of nonlinear fil...
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear ...
The objective of this research is to develop robust and accurate tracking algorithms for various tra...
This line of research seeks to increase knowledge of a tracked target using the particle filter, als...
The focus of the thesis is on developing distributed estimation algorithms for systems with nonlinea...
The extended Kalman filter (EKF) has been used as the standard technique for performing recursive no...
We propose an efficient SMC-PHD filter which employs the Kalman-gain approach during weight update t...
This article proposes a Gaussian filtering method to approximate the single-target updates and norma...
AbstractFor nonlinear state space models to resolve the state estimation problem is difficult or the...
Over the past few decades, the computational power has been increasing rapidly. With advances of the...
Abstract Distributed linear estimation theory has received increased attention in recent years due t...
Abstract—Increasingly, for many application areas, it is becoming important to include elements of n...
abstract: The tracking of multiple targets becomes more challenging in complex environments due to t...
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem ...
In this paper we study the problem of tracking maneuvering targets in the framework of nonlinear fil...
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear ...
The objective of this research is to develop robust and accurate tracking algorithms for various tra...
This line of research seeks to increase knowledge of a tracked target using the particle filter, als...
The focus of the thesis is on developing distributed estimation algorithms for systems with nonlinea...
The extended Kalman filter (EKF) has been used as the standard technique for performing recursive no...
We propose an efficient SMC-PHD filter which employs the Kalman-gain approach during weight update t...
This article proposes a Gaussian filtering method to approximate the single-target updates and norma...
AbstractFor nonlinear state space models to resolve the state estimation problem is difficult or the...
Over the past few decades, the computational power has been increasing rapidly. With advances of the...
Abstract Distributed linear estimation theory has received increased attention in recent years due t...
Abstract—Increasingly, for many application areas, it is becoming important to include elements of n...
abstract: The tracking of multiple targets becomes more challenging in complex environments due to t...
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem ...
In this paper we study the problem of tracking maneuvering targets in the framework of nonlinear fil...
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gauss...
The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear ...