  This paper examines the spatial coverage optimization problem for multiple
sensors in a known convex environment, where the coverage service of each
sensor is heterogeneous and anisotropic. We introduce the Stein Coverage
algorithm, a distribution-matching coverage approach that aims to place sensors
at positions and orientations such that their collective coverage distribution
is as close as possible to the event distribution. To select the most important
representative points from the coverage event distribution, Stein Coverage
utilizes the Stein Variational Gradient Descent (SVGD), a deterministic
sampling method from the variational inference literature. An innovation in our
work is the introduction of a repulsive force between the samples in the SVGD
algorithm to spread the samples and avoid footprint overlap for the deployed
sensors. After pinpointing the points of interest for deployment, Stein
Coverage solves the multisensor assignment problem using a bipartite optimal
matching process. Simulations demonstrate the advantages of the Stein Coverage
method compared to conventional Voronoi partitioning multisensor deployment
methods.
