A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Approximate distributed Kalman filtering in sensor networks with quantifiable performance
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Polynomial filtering for fast convergence in distributed consensus
IEEE Transactions on Signal Processing
Sensor Networks With Random Links: Topology Design for Distributed Consensus
IEEE Transactions on Signal Processing - Part II
SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Distributing the Kalman Filter for Large-Scale Systems
IEEE Transactions on Signal Processing - Part I
Distributed Kalman filtering based on consensus strategies
IEEE Journal on Selected Areas in Communications
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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 spectrumin order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute to significant energy saving. In order to implement the DKF in WSN, more power saving is needed. Since multiplication is the atomic operation of Kalman filter, so saving power at the multiplication level can significantly impact the energy consumption of the DKF. This paper also proposes a novel light-weight and low-power multiplication algorithm. The proposed algorithm aims to decrease the number of instruction cycles, save power, and reduce the memory storage without increasing the code complexity or sacrificing accuracy.