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This paper focuses on the problem of the distributed estimation of a parameter vector based on noisy observations regularly acquired by the nodes of a wireless sensor network and assuming that some of the nodes have faulty sensors. We propose two online schemes, both centralized and distributed, based on the Expectation-Maximization (EM) algorithm. These algorithms are able to identify and disregard...
The challenge of target tracking is one of the most important applications of WSNs (Wireless Sensor Networks). Traditionally, Kalman filter and its derivatives are some of the most popular algorithms for solving the signal tracking problem. In a WSNs tracking application, the target motion and state update dynamics might be modelled by linear or non-linear structures depending on the specific scenario...
Nonparametric belief propagation (NBP) is the well-known method for cooperative localization in wireless sensor networks. It is capable to provide information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based...
In sensor networks, data collected by sensor nodes needs to be tagged with time and location information. Localization techniques are used to determine the location information by estimating location of a sensor node. It is well known that distance measurement errors affect the accuracy of estimated location. These errors may be due to methodical or environmental factors. In this paper, we propose...
This paper addresses the model-based localization of sensor networks based on local observations of a distributed phenomenon. For the localization process, we propose the rigorous exploitation of strong mathematical models of distributed phenomena. By unobtrusively exploiting background phenomena, the individual sensor nodes can be localized by only observing its local surrounding without the necessity...
This paper presents a method for the simultaneous state and parameter estimation of finite-dimensional models of distributed systems monitored by a sensor network. In the first step, the distributed system is spatially and temporally decomposed leading to a linear finite-dimensional model in state space form. The main challenge is that the simultaneous state and parameter estimation of such systems...
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