Distributed estimation algorithms have attracted a lot of attention in the past few years, particularly in the framework ofWireless\nSensor Network (WSN). Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for\nscalable wireless sensor fusion.Most DKF methods proposed in the literature rely on consensus filters algorithm. The convergence\nrate of such distributed consensus algorithms typically depends on the network topology. This paper proposes a low-power DKF.\nThe proposed DKF is based on a fast polynomial filter. The idea is to apply a polynomial filter to the network matrix that will shape\nits spectrumin order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute\nto significant energy saving. In order to implement the DKF in WSN, more power saving is needed. Since multiplication is the\natomic operation of Kalman filter, so saving power at the multiplication level can significantly impact the energy consumption of\nthe DKF. This paper also proposes a novel light-weight and low-power multiplication algorithm. The proposed algorithm aims to\ndecrease the number of instruction cycles, save power, and reduce the memory storage without increasing the code complexity or\nsacrificing accuracy.
Loading....