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We present an innovative approach to distributed estimation which features time- and coefficient-selective updates of parameter estimates, thereby offering a significant reduction in energy consumption in the sensor nodes. The proposed approach is based on the principles of set-membership adaptive filtering (SMAF), which allows for selective updates of parameter estimates. It also employs the principle...
This paper presents an innovative approach to distributed estimation which features partial updates of parameter estimations, thereby offering significant reduction in energy consumption in the sensors. The proposed estimation scheme updates only a subset of the parameters at each iteration. This not only reduces data processing complexity, but also reduces the energy required for diffusing the parameter...
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...
This paper proposes selective update and cooperation strategies for parameter estimation in distributed adaptive sensor networks. A set-membership filtering approach is employed that results in reduced complexity for updating parameter estimates at each network node, a significant reduction in information exchange between cooperating nodes, and an optimal strategy to obtain consensus estimates. The...
In this work, a scheme for the calibration of an ADC model is presented, as well as the process of identification of the compensation block. The model proposed for this compensation block is a Hammerstein box model whose parameters are estimated in a non iterative fashion. A Hammerstein model is composed of a static nonlinearity followed by a linear time invariant (LTI) filter. This model is then...
This paper proposes a clustering approach to parameter estimation in distributed sensor networks. The proposed approach is an alternative to the conventional centralized and decentralized approaches. This is made possible by the unique adaptive estimation architecture, U-SHAPE, stemming from set-membership adaptive filtering. At the expense of a slightly degraded mean-square error performance (comparing...
This paper proposes a practical receiver cancellation technique for removing nonlinear power amplifier (PA) distortion in OFDM systems. By performing the estimation of the PA model parameters at the receiver, the implementation complexity of the transmitter can be reduced. Furthermore, a simple adaptation rule is provided to enable tracking of the PA model parameters. As a consequence, cancellation...
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