In this paper we consider state estimation of a discrete time linear system using multiple sensors, where the sensors quantize their individual innovations, which are then combined at the fusion center to form a global state estimate. We obtain an asymptotic approximation for the error covariance matrix that relates the system parameters and quantization levels used by the different sensors. Numerical results show close agreement with the true error covariance for quantization at high rates. An optimal rate allocation problem amongst the different sensors is also considered
Abstract – In this paper, we first present a general data model for discretized asynchronous multise...
In this paper, we consider optimal linear sensor fusion for obtaining a remote state estimate of a l...
This paper considers state estimation of hidden Markov models by sensor networks. By employing feedb...
In this paper we consider state estimation of a discrete time linear system using multiple sensors, ...
In this paper we consider state estimation of an unstable scalar system using multiple sensors, wher...
This paper considers state estimation of scalar linear systems using analog amplify and forwarding w...
In this paper, we consider the problem of state estimation for linear discrete-time dynamic systems ...
We consider remote state estimation of a scalar stationary linear Gauss-Markov process observed via ...
Abstract: In this paper we consider the problem of state estimation for linear dynamic systems using...
This paper considers the state estimation of hidden Markov models by sensor networks. The objective ...
WeA05 Plumeria 2 - Estimation Problems I (Regular Session): no. WeA05.3The fusion estimation is inve...
This paper investigates the problem of a scalable distributed state estimation for a class of discre...
Abstract—This paper considers the state estimation of hidden Markov models by sensor networks. The o...
This paper considers the state estimation of hidden Markov models by sensor networks. The objective...
Schemes for quantization and fusion in multi-sensor systems used for discriminating between two sequ...
Abstract – In this paper, we first present a general data model for discretized asynchronous multise...
In this paper, we consider optimal linear sensor fusion for obtaining a remote state estimate of a l...
This paper considers state estimation of hidden Markov models by sensor networks. By employing feedb...
In this paper we consider state estimation of a discrete time linear system using multiple sensors, ...
In this paper we consider state estimation of an unstable scalar system using multiple sensors, wher...
This paper considers state estimation of scalar linear systems using analog amplify and forwarding w...
In this paper, we consider the problem of state estimation for linear discrete-time dynamic systems ...
We consider remote state estimation of a scalar stationary linear Gauss-Markov process observed via ...
Abstract: In this paper we consider the problem of state estimation for linear dynamic systems using...
This paper considers the state estimation of hidden Markov models by sensor networks. The objective ...
WeA05 Plumeria 2 - Estimation Problems I (Regular Session): no. WeA05.3The fusion estimation is inve...
This paper investigates the problem of a scalable distributed state estimation for a class of discre...
Abstract—This paper considers the state estimation of hidden Markov models by sensor networks. The o...
This paper considers the state estimation of hidden Markov models by sensor networks. The objective...
Schemes for quantization and fusion in multi-sensor systems used for discriminating between two sequ...
Abstract – In this paper, we first present a general data model for discretized asynchronous multise...
In this paper, we consider optimal linear sensor fusion for obtaining a remote state estimate of a l...
This paper considers state estimation of hidden Markov models by sensor networks. By employing feedb...