It can be difficult to calibrate the three-dimensional (3D) optical measurement network (OMN) designed to inspect large-scale and shell-like objects.
RDF can be used to estimate missing data, determine the location and optimum number of measurement stations (station network design), estimate the potential of a variable under consideration and calculate radius of influence.
The motion and visibility of the satellites w.r.t. each other in the constellation have to be evaluated as an prerequisite to many tasks, especially to the inter-satellite network design, measurement scheduling and pointing-related issues.
This paper deals with the problem of where to place the cameras in order to obtain a minimal error in the 3D measurements, also called camera network design in photogrammetry.
Two different strategies are followed: the design of a measurement network that aims at minimizing the log-transmissivity variance (averaged over the domain of interest) or a design that minimises the hydraulic head variance (averaged over the domain of interest).
The novelty here is the approach to the "pre-processing" phase of a sensor network deployment, involving working environment constraints, system functional characteristics, measurand geometry, and measurement task definition in the three-dimensional network design.
The method is conceived for the network design stage, when network measurements are still not available.
Field measurements have always been the starting point for network design and planning; however, their statistical analysis beyond simple traffic volume estimation is not so common.
Accurate measurement and modeling of IP networks is essential for network design, planning, and management.
Location of sensors, cost of measurement and frequency of sampling are important factors that have been incorporated in the sensor network design formulation.
