Adaptive Filters
Anytime Optimal Distributed Kalman Filtering and Smoothing
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Diffusion least-mean squares with adaptive combiners
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Distributing the Kalman Filter for Large-Scale Systems
IEEE Transactions on Signal Processing - Part I
ACM Transactions on Sensor Networks (TOSN)
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We study the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their measurements. In diffusion Kalman filtering strategies, neighboring state estimates are linearly combined using a set of scalar weights. In this work we show how to optimally select the weights, and propose an adaptive algorithm to adapt them using local information at every node. The algorithm is fully distributed and runs in real time, with low processing complexity. Our simulation results show performance improvement in comparison to the case where fixed, non-adaptive weights are used.