  Wireless sensor networks consist of sensor nodes that are physically
distributed over different locations. Spatial filtering procedures exploit the
spatial correlation across these sensor signals to fuse them into a filtered
signal satisfying some optimality condition. However, gathering the raw sensor
data in a fusion center to solve the problem in a centralized way would lead to
high energy and communication costs. The distributed adaptive signal fusion
(DASF) framework has been proposed as a generic method to solve these signal
fusion problems in a distributed fashion, which reduces the communication and
energy costs in the network. The DASF framework assumes that there is a common
goal across the nodes, i.e., the optimal filter is shared across the network.
However, many applications require a node-specific objective, while all these
node-specific objectives are still related via a common latent data model. In
this work, we propose the DANSF algorithm which builds upon the DASF framework,
and extends it to allow for node-specific spatial filtering problems.
