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Identifying and detecting the unknown abnormal sparse signal has become an important issue for distributed networks. In this paper, we proposed a new detection scheme based on convex optimization for wireless sensor networks. Under the Neyman-Pearson testing framework, the detection scheme first estimates the unknown signal by employing the convex optimization at the fusion center. Then the sensor...
We consider a problem of significant practical importance, namely, the reconstruction of a low-rank data matrix from a small subset of its entries. This problem appears in many areas such as collaborative filtering, computer vision and wireless sensor networks. In this paper, we focus on the matrix completion problem in the case when the observed samples are corrupted by noise. We compare the performance...
In this paper, we consider decentralized estimation of a noise-corrupted deterministic parameter in wireless sensor networks with the aid of relay. We propose a new relay-aided decentralized estimation scheme by which relay collects the overheard messages from sensors, computes a local message, and then sends it to a destination. Besides, we develop the sensor selection policies to select the most...
Distributed sensor networks employ multiple nodes to collectively estimate or track parameter(s) of interest without any central fusion node. Individual nodes may observe (sense) and estimate the parameter of concern as well as cooperate with other nodes to arrive at a global consensus estimate. We propose a simple heuristic algorithm using a set-membership filtering approach to adaptively determine...
For a large and dense outdoor sensor network, the impact of sensor density and signal to noise ratio (SNR) are investigated on the performance of a maximum likelihood (ML) location estimation algorithm. The ML estimator fuses data, in the form of signal amplitudes, transmitted from local sensors to estimate the location of a source. A Gaussian-like isotropic signal decay model is adopted to make the...
In this paper we consider the problem of estimating the eigenvectors of the sample covariance matrix of decentralized measurements in a distributed fashion. The need for a distributed scheme is motivated by the many moment based methods that resort to the covariance of the data to extract information from the measurements. For large sensor network, gathering the data at a central processor generates...
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