Low-Power Distributed Kalman Filter for Wireless Sensor Networks

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Distributed estimation algorithms have attracted a lot of attention in the past few years, particularly in the framework of Wireless Sensor Network (WSN). Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. Most DKF methods proposed in the literature rely on consensus filters algorithm. The convergence rate of such distributed consensus algorithms typically depends on the network topology. This paper proposes a low-power DKF. The proposed DKF is based on a fast polynomial filter. The idea is to apply a polynomial filter to the network matrix that will shape its spectrum in order to increase the convergence rate by minimizing its second largest eigenvalue.