2 research outputs found
Distributed Kalman Estimation with Decoupled Local Filters
We study a distributed Kalman filtering problem in which a number of nodes
cooperate without central coordination to estimate a common state based on
local measurements and data received from neighbors. This is typically done by
running a local filter at each node using information obtained through some
procedure for fusing data across the network. A common problem with existing
methods is that the outcome of local filters at each time step depends on the
data fused at the previous step. We propose an alternative approach to
eliminate this error propagation. The proposed local filters are guaranteed to
be stable under some mild conditions on certain global structural data, and
their fusion yields the centralized Kalman estimate. The main feature of the
new approach is that fusion errors introduced at a given time step do not carry
over to subsequent steps. This offers advantages in many situations including
when a global estimate in only needed at a rate slower than that of
measurements or when there are network interruptions. If the global structural
data can be fused correctly asymptotically, the stability of local filters is
equivalent to that of the centralized Kalman filter. Otherwise, we provide
conditions to guarantee stability and bound the resulting estimation error.
Numerical experiments are given to show the advantage of our method over other
existing alternatives
Distributed Filter with Consensus Strategies for Sensor Networks
Consensus algorithm for networked dynamic systems is an important research problem for data fusion in sensor networks. In this paper, the distributed filter with consensus strategies known as Kalman consensus filter and information consensus filter is investigated for state estimation of distributed sensor networks. Firstly, an in-depth comparison analysis between Kalman consensus filter and information consensus filter is given, and the result shows that the information consensus filter performs better than the Kalman consensus filter. Secondly, a novel optimization process to update the consensus weights is proposed based on the information consensus filter. Finally, some numerical simulations are given, and the experiment results show that the proposed method achieves better performance than the existing consensus filter strategies