This paper presents a new method to solve a dynamic sensor fusion problem. We
consider a large number of remote sensors which measure a common Gauss-Markov
process and encoders that transmit the measurements to a data fusion center
through the resource restricted communication network. The proposed approach
heuristically minimizes a weighted sum of communication costs subject to a
constraint on the state estimation error at the fusion center. The
communication costs are quantified as the expected bitrates from the sensors to
the fusion center. We show that the problem as formulated is a
difference-of-convex program and apply the convex-concave procedure (CCP) to
obtain a heuristic solution. We consider a 1D heat transfer model and 2D target
tracking by a drone swarm model for numerical studies. Through these
simulations, we observe that our proposed approach has a tendency to assign
zero data rate to unnecessary sensors indicating that our approach is sparsity
promoting, and an effective sensor selection heuristic