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In this paper, a new framework for sequential Bayesian estimation in sensor networks is proposed, which consists of two processes: censoring of measurements at local sensors and fusion of both received measurements and missing ones at the fusion center (FC). In our scheme, each local sensor maintains a Kalman filter (KF) for a linear Gaussian system or an(More)
—The recursive procedure to compute the posterior Cramér-Rao lower bound (PCRLB) for sequential Bayesian estima-tors, derived by Tichavsky et al., provides an off-line performance bound for a general nonlinear filtering problem. Since the corresponding Fisher information matrix (FIM) is obtained by taking the expectation with respect to all the random(More)
In this paper, a new framework for sequential Bayesian estimation in a sensor network by using both the received data and the information conveyed by missing data due to per-sensor censoring is proposed. In this framework, each local sensor maintains a Kalman Filter (KF) and the Fusion Center (FC) runs a particle filter (PF) to track the system state.(More)
In this paper, we consider the problem of sensor management for target tracking in a wireless sensor network (WSN). To determine the set of sensors that have the most information, we develop a probabilistic sensor management scheme based on the concepts developed in compressive sensing. In the proposed scheme, each senor node decides whether it should(More)
For a large and dense sensor network, the impact of sensor density is investigated on the performance of a maximum likelihood (ML) location estimator using quantized sensor data. The ML estimator fuses quantized data transmitted from local sensors to estimate the location of a source. A Gaussian-like isotropic signal decay model is adopted to make the(More)
In this paper, we consider a Bayesian estimation problem in a sensor network where the local sensor observations are quantized before their transmission to the fusion center (FC). Inspired by Widrow's statistical theory on quantization, at the FC, instead of fusing the quantized data directly, we propose to fuse the post-processed data obtained by adding(More)
In this paper, we consider the problem of sensor management for target tracking in a wireless sensor network (WSN). To determine the set of sensors with the most informative data, we develop a probabilistic sensor management scheme based on the concepts developed in compressive sensing. In the proposed scheme where each sensor transmits its observation with(More)
The recursive procedure to compute the posterior Cramér-Rao lower bound (PCRLB) for sequential Bayesian estimators, derived by Tichavsky , provides an off-line performance bound for a general nonlinear filtering problem. Since the corresponding Fisher information matrix (FIM) is obtained by taking the expectation with respect to all the random(More)
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