Algorithmic correlation: Is the degree of informational consistency between Parametric correlation: Is the degree of informational consistency between
48 © 2015 Open Geospatial Consortium
error by exercising explicit control, at run-time, on the resolution of the data processed by the client-devices. This approach is much preferable to off-line
published approaches where parametric correlation is established during the compilation of the runtime databases and cannot be changed once the runtime
database is produced.
Table 1-1: Summary of Synthetic Environment Database Correlation Errors, provides a summary of the different types of correlation errors and howwhere such errors can be addressed. Figure
1-15: Sources of Synthetic environment Database Correlation Errors, illustrates the conventional synthetic environment database process from raw source, the assembly of datasets at the DB
workstation, the publishing into runtime databases, and the rendering by the simulator client- devices.
Table 1-1: Summary of Synthetic Environment Database Correlation Errors
Correlation Description
Addressed by C
D B
S p
e c
S im
D e
vi c
e s
D a
ta b
a se
T o
o ls
Data Raw Source
Raw source correlation errors caused by data
collections at different times and from different
devices. Also caused by registration errors.
Toolset and human operators address raw
source correlation errors ensuring that data is
properly corrected and registered.
X
Datasets Dataset correlation errors
are caused by discrepancies between co-located dataset
layers. Toolset ensures that
dependent dataset layers are updated together,
while CDB Specification minimizes these
dependencies. X
X
Runtime Sources
Information from several runtime databases contains
conflicting information. CDB Specification
addresses runtime source correlation by enforcing
a single runtime database.
X
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Correlation Description
Addressed by C
D B
S p
e c
S im
D e
vi c
e s
D a
ta b
a se
T o
o ls
Computational Algorithmic
Different client-devices may use different
algorithms to filter information and to simulate
real-world device. Simulation clients
address algorithmic correlation errors. They
ensure that the different algorithms used are
compatible. X
Parametric Same client-devices may
run with different initial parameters.
A simulation that would ensure each client-
device runs at just discernible error levels.
X
Accuracy Different computational
platforms may run at different numerical
accuracies. Using the same platform
with identical numerical accuracy for all client-
devices and servers. Developing software
according to strict rules and guidelines addresses
computational accuracy correlation errors.
X X
X
Temporal Synchronism
Lags and delays introduced by networking systems as
well as subsystems using different time bases may
cause subsystems to be unsynchronized.
Through system architecture, system is
designed to use proper bandwidths and
computational methods to reduce latencies. All
simulator client-devices use the same clock.
X
Paging latency Paging and runtime publishing may introduce
delays long enough to prevent some simulation
clients to get information on time.
Paging and runtime publishing tasks and
client-device paging software ensures that
runtime information is available on time.
X
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Figure 1-15: Sources of Synthetic environment Database Correlation Errors
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The implementation of the CDB Specification on a simulator can also provide the means to reduce and control the sources of parametric correlation at the client-device level. The
underlying cause of parametric correlation usually points to the intrinsic capabilities i.e., the performance and functionality of each client-device. Since many client-devices are unable to
cope with the synthetic environment database at its full fidelity, the off-line compilers “filter- out” portions of the database e.g., level of resolution, level of fidelity in order to meet the
limited functionality or the real-time constraints of the targeted device. In order to further control processing variations, some client-devices are equipped with the means to dynamically
“filter-out” portions of the database in order to meet real-time constraints. The filters can be typically controlled statically and sometimes even dynamically through the use of parameters.
The amount and type of data that is rejected by each type of client-device can vary considerably. This is the underlying cause of parametric correlation errors. In conventional simulator client-
devices, the handling of parametric correlation is handled in a half-hazard fashion, largely because the “filter parameters” are either fixed or inaccessible to the user. Distinct database
compilers generate distinct runtime databases, each configured with their respective filtering parameters.
The CDB Specification offers multi-tiered solutions to this problem. Firstly, since it has a unique data representation, the resolution, fidelity and accuracy of the synthetic environment
database, as seen by each of runtime publisher attached to the client-devices, is completely correlated. Secondly, since the publishing process is done on-line, it is possible to provide the
user access to the filter parameters so that he can globally control resolution, fidelity and accuracy and hence correlation of the synthetic environment database across all simulator
client-devices. While the CDB Specification does not provide explicit jurisdiction over the implementation of this mechanism in the client-devicespublishers, it is nonetheless possible to
improve parametric correlation, at runtime, via control of the client-devicespublishers. This new paradigm now permits the simulator user the means to not only control client-device load
but to globally re-examine and control the level of correlation within a simulator or across simulators.
The control of parametric correlation requires a working understanding of the characteristics of the client-device. At a minimum, one must consider the operating limits of all client-devices and
ensure that the runtime publishers limit synthetic environment content to the limits imposed by the client-devices
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. Secondly, one must have a working model of the “filter parameters” made available by the client-devices or by their runtime publishers. Thirdly, one must have a working
performance model of each client-device. Finally, one must have an understanding of “just discernible threshold” of correlation as it applies to each client-device. For example, it is
unlikely that increasing the terrain resolution from 1m to ½m would produce a discernible
change in how a CGF system allows simulated players to interact realistically with one another. As a result, the
½m terrain resolution of the CGF system is below the “just discernible threshold” of that device. The
½m data can be discarded without reducing the level of correlation provided by that client-device.
22
For instance, an IG might refuse to operate if confronted with an area modeled with 1 millimeter OTW texture.
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As mentioned earlier, the runtime publishing paradigm permits the simulator user the means to control client-device load and to globally re-examine and control the level of correlation within a
simulator or across simulators. Several choices are possible, depending on the flexibility offered in the implementation of the runtime publishers and the client-devices. The choices are:
1. Publishers globally adjusted to overload limit of least capable client-device see Figure 1-16: Overload Limit of Least Capable Client-Device:
Figure 1-16: Overload Limit of Least Capable Client-Device
a. Assuming sufficient parameter controls in each of the client-devices, parametric correlation errors can be eliminated by setting the filter
parameters of all client-devicespublishers to the least-capable client- device in the simulator.
b. Success is contingent on suitable set of parameters in each client- device.
c. The result is full correlation with no overloads, but with the lowest observed DB fidelity.
2. Publishers individually adjusted to the overload limit of each client device see Figure 1-17: Overload Limit of Each Client-Device:
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Figure 1-17: Overload Limit of Each Client-Device
a. This is the approach used in virtually all simulators in operation today. b. Result is partial correlation with no overloads, and high-observed DB
fidelity. 3. Publishers adjusted to the CDB content, but globally adjusted to the operating
limit of the least capable client-device see Figure 1-18: Operating Limit of Least Capable Client-Device:
Figure 1-18: Operating Limit of Least Capable Client-Device
a. Client-devices are allowed to overload in areas where database content is deemed critical.
b. Result is full correlation, possible overloads with high-observed DB fidelity.
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4. Publishers adjusted to the CDB content, but individually adjusted to the operating limit of each client-device see Figure 1-19: Individually Adjusted
to the Operating Limit of Each Client-Device:
Figure 1-19: Individually Adjusted to the Operating Limit of Each Client-Device
a. Client-devices are allowed to overload in areas where database content is deemed critical.
b. Result is, possible overloads and the highest observed DB fidelity. 5. Publishers adjusted by the simulator operator at scenario startup see Figure
1-20: Adjusted by the Simulator Operator at Scenario Startup:
Figure 1-20: Adjusted by the Simulator Operator at Scenario Startup
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Chapter 2
2
CDB Concepts
This chapter presents basic CDB concepts of the Specification. These concepts are either reused by other concepts or used repeatedly throughout the Specification.