Interpolation techniques for building a continuous map from discrete wireless sensor network data
Scattered data interpolation and approximation problems arise in many applications. Shepard's method for constructing global interpolants by blending local interpolants using locally-supported weight functions usually creates reasonable approximations. This paper describes SHEPPACK, a Fortran 95 package containing five versions of the modified Shepard algorithm. These five versions include quadratic (TOMS Algorithm 660, 661, and 798), cubic (TOMS Algorithm 790), and linear variations of the original Shepard algorithm. The main goal of SHEPPACK is to provide users with a single consistent package consisting of all existing polynomial variations of Shepard's algorithm. The algorithms target data of different dimensions. The linear Shepard algorithm is the only algorithm in the package that is applicable to arbitrary dimensional data. The motivation is to enable researchers to experiment with different algorithms using their data and select one (or more) that is best suited to their needs, and to support interpolation for sparse, high dimensional data sets.
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