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Kalman Filter

Chapter
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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 118)

Abstract

This chapter has briefly discussed the need of the DKF and introduced the literature work of the DKF. 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 and the weights given to the edges between neighboring sensors. The next chapter proposes a low power DKF. The proposed DKF is based on a fast polynomial filter to accelerate distributed average consensus in static network topologies. The idea is to apply a polynomial filter on the network matrix that will shape its spectrum in order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute to significant energy saving.

Keywords

Sensor Network Wireless Sensor Network Hide Markov Model Kalman Filter Adjacency Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Essex JunctionUSA
  2. 2.University of Louisiana at LafayetteLafayetteUSA

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