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We consider a new inference-centric application for a distributed sensor network. Consider multiple signal sources, i.e., acoustic sources, in the field being monitored. Governed by physics law each sensorpsilas measurement can be modeled as a linear combination of the original multiple signal sources, corrupted by the additive measurement noise. In a non-cooperative communication scenario, each sensor...
In this paper, we consider the distributed parameter estimation problem using one-bit quantized data from local sensors. Nonparametric distributed estimators are proposed based on knowledge of the moments of sensor noise. These estimators are shown to be either unbiased or asymptotically unbiased with bounded estimation variance for all possible parameter values. Relationship between the proposed...
Non-parametric estimation of an unknown position parameter in a bandwidth-constrained wireless sensor network (WSN) is considered in this paper. Due to bandwidth constraint, each sensor is restricted to send only one bit of information to a fusion center. We propose a non-parametric estimator that employs a recently introduced adaptive quantization (AQ) scheme. Specifically, the position parameter...
While elegant in form, the maximum likelihood estimator (MLE) for heavily bandwidth-constrained distributed estimation in Gaussian noise is computationally expensive to implement. We consider an alternative estimator for this case which requires far less computational complexity, yet performs close to the MLE under the same operating conditions.
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