Source database self-correlation: Is the degree of informational consistency

47 © 2015 Open Geospatial Consortium devices computing the sin of an angle, one with a series of 10 terms, and another with an interpolation of a look-up table with 100 entries, or one device using 32-bit signed integers for its internal computations and the other using single-precision floats. The CDB Specification reduces numerical accuracy correlation errors because a single representation is used for each dataset. Note however that numerical correlation may deteriorate due to the variances in numerical precision inherent to each client-device.

5. Algorithmic correlation: Is the degree of informational consistency between

the outputs of two or more devices, with each device performing its internal computations to the same numerical accuracy, but using different algorithms with possibly different control parameters. Consider for example, two devices meshing terrain from a regular grid of elevation points, one using a regular mesh of right-handed triangles using the elevation points as vertices, and the other with a DeLauney triangulated mesh derived from the grid of elevation points. Algorithmic correlation errors can be introduced anywhere in the dataset processing chain, ranging from the raw source level right through to the internals of the simulator client-devices. While it is not possible to mandate complete algorithmic uniformity throughout this elaborate chain, the CDB Specification offers solutions to this issue 20 . Firstly, only one database is produced, so all DB authoring tool algorithmic correlation issues are eliminated. Note that the implementation of runtime publishers on a simulator can play a role in improving overall algorithmic correlation 21 .

6. Parametric correlation: Is the degree of informational consistency between

the outputs of two or more devices, with each device performing the same algorithms with identical numerical precision with different control parameters e.g., consider two devices generating regular meshes of right- handed triangles based on a regular grid of elevation points organized by LOD, one using an LOD meshing tolerance parameter of 1m and the other one using 2m. Note that it is clearly possible to create a synthetic environment database whose content is at a level of resolution, fidelity and accuracy that can overwhelm any client-device; however, the amount and type of data that is rejected by each type of client-device can vary considerably. For example, a NAVAIDs data server client-device need only be concerned with Digital Aeronautical Flight Information File DAFIF™, and as a result is virtually unaffected by changes in the resolution of the terrain elevation data which could easily increase terrain SE content by 100-fold. The implementation of the CDB Specification on a simulator can reduce andor eliminate parametric 20 For instance, the CDB does not impose the terrain skinning and meshing conventions that each of the runtime publisher generates for its attached client-device. However, it is understood that if all client-devices would use the CDB meshing conventions natively, correlation issues between clients would be significantly reduced. 21 Since each dataset is uniquely represented, it is possible to share a greater number of algorithms between CDB runtime publishers. This ensures that datasets are processed in an identical fashion whenever two or more publishers share the same algorithm.