In this paper, a model based sensor fusion algorithm for sensor networks is presented. The algorithm, referred to as distributed Kalman filtering is based on a previously presented algorithm with the same name. The weight selection process has been improved yielding performance improvements of several times for the examples studied. Also, solutions to both optimization problems involved in the iterative off-line weight selection process are given as closed form expressions. The algorithm is also demonstrated on a typical signal tracking application
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem ...
Distributed target tracking using sensor networks is crucial for supporting a variety of application...
In the paper, fusion state hierarchical filtration for a multisensor system is considered. An optima...
Abstract: In this paper, a model based sensor fusion algorithm for sensor networks is presented. The...
In this paper, we propose a strategy for distributed Kalman filtering over sensor networks, based on...
In this paper, we propose a strategy for distributed Kalman filtering over sensor networks, based on...
In this work, a distributed Kalman filtering and clustering framework for sensor networks tasked wit...
The assessment of dynamic situations using data from multiple sensors occurs in many military and ci...
In this paper,we propose a distributed Kalman Filter based algorithm,known in literature as Consensu...
In this paper, various techniques for information fusion in distributed sensor applications are pres...
In this paper, we present a robust distributed fusion algorithm to handle intermittent observations ...
Accurate location information plays an important role in the performance of wireless sensor networks...
Most distributed Kalman filtering (DKF) algorithms for sensor networks calculate a local estimate of...
Sensor fusion is a method of integrating signals from multiple sources. It allows extracting informa...
For multisensor data fusion, distributed state estimation techniques that enable a local processing ...
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem ...
Distributed target tracking using sensor networks is crucial for supporting a variety of application...
In the paper, fusion state hierarchical filtration for a multisensor system is considered. An optima...
Abstract: In this paper, a model based sensor fusion algorithm for sensor networks is presented. The...
In this paper, we propose a strategy for distributed Kalman filtering over sensor networks, based on...
In this paper, we propose a strategy for distributed Kalman filtering over sensor networks, based on...
In this work, a distributed Kalman filtering and clustering framework for sensor networks tasked wit...
The assessment of dynamic situations using data from multiple sensors occurs in many military and ci...
In this paper,we propose a distributed Kalman Filter based algorithm,known in literature as Consensu...
In this paper, various techniques for information fusion in distributed sensor applications are pres...
In this paper, we present a robust distributed fusion algorithm to handle intermittent observations ...
Accurate location information plays an important role in the performance of wireless sensor networks...
Most distributed Kalman filtering (DKF) algorithms for sensor networks calculate a local estimate of...
Sensor fusion is a method of integrating signals from multiple sources. It allows extracting informa...
For multisensor data fusion, distributed state estimation techniques that enable a local processing ...
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem ...
Distributed target tracking using sensor networks is crucial for supporting a variety of application...
In the paper, fusion state hierarchical filtration for a multisensor system is considered. An optima...