A Method for Data Interaction of Large-Scale Distributed Battle ...

A Method for Data Interaction of Large-Scale Distributed Battle Simulation System
Cheng Zhi-feng, Xing Chang-feng, Liu Gao-feng
College of Electronic Engineering
Naval University of Engineering
Wuhan, China
E-mail: cheryfig@sina.com,xingchf@sohu.com,gaofengliu_17@sina.com
Abstract—Aiming at currently data interaction bottleneck
problem in large-scale distributed battle simulation system, a
new data interactive method which based on layeringsynchronizing-sharing
policy was proposed. Different from the
traditional entity-centric data exchange thought, it was
established at the thought of entity classification and situation
layering. The outputs of entities model form a situation layer.
Each situation layer is a global virtual environment. Data
interaction task was Undertook between entities and virtual
environment. It is very effective to reduce interaction logic
complexity and data quantity. This method may also reduce
the entity coupling degree to be connected, and is helpful to
entity mode’s verification, simulation system's dynamic
extension and cross-domain integration.
Keywords- Computer application; Distributed interactive
simulation; Battle space entity; Data interaction; Layering -
synchronizing - sharing
I. INTRODUCTION
Battle simulation system is an important means to
develop battle training and battle method research. It is
regarded for its agile training subjects setting and low
organizing cost. Actually it forms distributed online systems
with simulation models, computers, instruments and systems
through net, then complex distributed simulation systems
come into being which is distributed in area, dynamic
interaction and information online. Because battle process
deal with many entity types and complex information flow,
in order to describe the battle situation realistically, it need to
consider the attribute behaviors and their complex data
depend relations of BSE(Battle Space Entity), such as battle
platforms, sensors, weapon system, command posts and
communication nets. As the unceasing expand of the scale of
battle simulation systems simulation, time field and zone,
simulation systems are more and more complex. When
simulations are running, data and information’s interaction
and collaboration problem between entities, computers and
subsystems become more and more extrusive.
Based on HLA (High Level Architecture), with soft bus
method, connect simulation models correlative to the
scenario background with simulation members needed, then
form virtual war space helping to problem research. It is
mainstream techniques of current battle simulation. It uses
harmonious agreements, standards and structures to realize
mutual operation between different simulation systems. But
there are some problems in practice as follows: data
interaction speed is limited when the system scale is large;
the interaction cannot cross the net or in different area; nodes
coupling is too close to expand; simulation resources assign
is static. Before simulations start, resources have already tied
to simulations subtask and cannot change in simulations
running processes. So it is necessary to debase the net jam by
data interaction and reduce the workload of martial
simulation’s development, maintenance and expand through
topmost data organizing design. Through much development
processes of martial simulation systems, we summarize a
new data interactive frame in battle simulation system, it can
instruct the data interaction in higher level.
II. COLLECTIVITY IDEA
Whether the battle simulation can describe the battle
process truly? The key is whether the modeling of battle
organization structure and running mechanism can reflects
the external situation truly and integrally. A full battle
process contains military actions, situation apperceiving,
information transmission and campaign commanding. The
campaign information (contain the battlefield situation
information campaign command information and etc.)
creation and transfer is a belt to communicate these teaches.
The essential of battle simulation is to simulate a series
of processes that the campaign information create, filtrate,
transform, transfer and utilize in physics, information and
cognizing field. The battle simulation come from apiece
types of sensors cognizing and judging to external
environment in physics field. Then they fuse in information
field to enter cognizing field or enter cognizing field directly.
At last, they form the base of cognizing and deciding in
cognizing field. The commander’s decisions and behaviors
have effect on the development of battlefield. On the other
hand they change the information entering the information
and cognizing field. They cycle in campaign process. Data
organization in battle simulations in this paper is the
campaign information organization in information and
cognizing field. Actions of information in cognizing field
are mainly around the commander’s decisions and these
actions are accomplished by people in loop in this paper.
A. Entity sorting
BSE contain platforms, sensors, command nodes,
communication nodes and etc. They all serve for the series of
processes which campaign information experience in physics,
information and cognizing field. In battle simulation, each
type of entities has some similar characteristics. As reference
[3] describing, in JWARS (Joint Warfare System) US army
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divided battlefield entity behaviors into six types: command
and control entity, sensor entity, resource management entity
and other entity. Based on the roles which the entities play in
information actions and the types of “Produced / Consumed”
data, they divided entity into four types: platform entity,
cognizing entity, communication entity and command entity.
In a word, the purpose of sorting is to organize data
interaction in class form.
1) Platform class entity: They indicate war platforms
such as warship, plane, missile and ect. In simulation these
entity main task is to simulate its behaviors such as platform
maneuvering and etc. It mainly create the position, state and
maneuvering and other data.
2) Cognizing class entity: It also called sensor entity
refers to various sensors with capabilities of detection,
including radar, sonar, electronic surveillance and other
equipment. In Simulation, the main task of such nodes is to
form a detection result information fighting platform based
on the objective situation information generated by the war
platform node. They subdivided into two types of cognizing
entities: active and passive sensing entities. Active sensing
entities affect objective environment when they are
detecting, such as radar; while passive sensing entities work
through analyzing objective environmental changes, such as
electronic surveillance equipment.
3) Command class entity: It refers to all kinds and levels
of ommand posts such as formate command posts, campaign
group command posts, war zone command posts and so on.
In simulation, the task of such nodes is to receive
transmitted data from communication nodes and form a
decision-making situation for the commander。
4) Communication class entity: It refers to the various
communication links for data transmission, including cable
links, satellite communication links, data links and so on. It
is responsible for transferring data from one/several entities
to another/several e entities. In simulation, such nodes’ task
is transferring the detect result information from the sensor
nodes to the command post nodes and transferring situation
information between the command post node. For example,
it transfers cognizing information from a radar entity to a
command entity. Modeling concerns communication
bandwidth, communication capabilities and so on.
This classification approach is consistent with the OODA
loop model (Observe, Orient, Decide, Act) made by John R.
Boyd. In this model, each type of entity can match a
command and control functions, as shown in Fig.1. As the
basic of command and control process, Communication is a
separately class.
In simulation, the various type of nodes exist fixed
supply and demand relationship, as shown in Tab. 1.
B. Situation layering
The purpose of developing battle simulation system is to
train military decision-making person’s ability of situation
cognition. Situation cognition is to grasp real-time elements
and patterns with the individual’s goals in the dynamical
strong change scenarios. In the actual campaign process,
situation is to display the battle situation images from the
output information of various types of sensors. It reflects the
current external environment, conditions, object state and
other elements relevant to decision-makers’ service goals. It
is a set of records of feature vectors of targets and element
state relevant to actions purpose, such as platform type,
location, speed and so on. It is based on the “cognizing facts”,
rather than “objective facts”. It is cognizing results of
cognizing entity to “objective facts”.
Figure 1. Entity’s Rule in OODA Model
TABLE I. DATA PRODUCED OR CONSUMED BY ENTITY SIMULATION
MODEL
Entity Type Production Data Consumption Data
Platform Entity
Platform Situation
Information
-
Cognizing Entity
Cognizing Situation Platform Situation
information
Information
Communication
Entity
Command Situation
Information
Cognizing Situation
Information,
Command Situation
Information
Command Entity Decision Conclusion Command Situation
Information
To simulation system, the battle simulation require
“objective situation” constituted by the "objective facts", "
cognizing situation " constituted by “cognizing facts” and
“command situation”. The same platform entity may form a
physical output in a number of cognizing entity. Meanwhile
a piece of cognizing information may be sent to several
command entities. Therefore, the interactive data volume is
an inverted pyramid structure, the toper the data are, the
more information need to deliver. As shown in Figure 2, a
large amount of data exchange make a large-scale battle
simulation system to be characterized as complex systems,
while the stratification is a common method to solve this
complex problem, such as the International Organization for
Standardization OSI (Open System Interconnect Reference
Model) 7-layer network protocols. According to the data
effectiveness in the simulation system, the situation data is
divided into the following three layers.
1) Objective situation layer: It refers to simulation of
BSE’s true state in the objective environment. It include
physical situation (visible), the electromagnetic situation
(invisible) and so on. It is the environment where various
types of sensors work in simulation. The U.S. military called
it SNE (Synthetic Natural Environment) [3].
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2) Cognizing situation layer: It refers to all kinds of
sensors with sensing capabilities which form cognizing
information through cognizing the objective situation.
3) Command situation layer: It refers to battlefield
situation for commander’s decision-making through
communication network transmission and the fusion nodes’
disposal.
Layer is a virtual concept with the form of data set. Each
layer is a DVE (Distributed Virtual Environment). In the
simulation process, entities continuously update the status,
all entities’ state information create a global, consistent, nonredundant
data sets, that is, a shared simulation of virtual
space.
C. Layering - synchronizing - sharing
The current large-scale simulation systems are usually
organized as a unit entity to data exchange, and made full use
of HLA / RTI (Runtime Infrastructure, the federal operating
support systems) to provide data exchange facilities.
However, there are still some problems, mainly reflected in
the following two aspects:
• The supply and demand data between the various
entities in simulation are complex. When there many
nodes in simulation, the network bandwidth become
a bottleneck.
• Federals coupled in high level depending on data
relations. So it is not easy to expand.
In data interaction which make "layer" as the center,
various types of simulation data between entities do not
directly interact, but establish supply and demand relation
with the corresponding data layer. Aimed at these three data
layers, we set data server separately for the unified
interaction between entities and layers. Firstly, entities
register to notify relevant server when state change occurs
(position change, destroyed, failure), and demand data
requirement in real-time. The servers accept all kinds of
entities’ state data, complete data update, and publish the
data according to the entities’ order.
Synchronization refers to data consistency management
for the data created by the internal running simulation
entities in one simulation node, and data consistency
management for data between several simulation nodes in
same types of servers. Several battle simulation systems can
be integrated with this method.
Figure 2. Examples for Three Posture Layers Data
Over use of this method, when integrated simulation
system, Data in same type of situation servers can be
transmitted in synchronization, then a distributed large-scale
battle simulation integrated system is formed.
Sharing is the means for consumer entities. Data of this
layer is fully shared. No matter how many simulation entities
are running in that node, all of them can share the overall
simulation data.
III. KEY TECHNOLOGY
Simulation program deployed in all nodes and servers
can be developed using the HLA simulation framework.
They can be existed as federations in HLA criterion and RTI
is used for the underlying communication of data. The data
interaction is implemented with HLA Subscribe / Publish
mechanisms. Various campaign platform simulation
federations publish entity objective situation to objective
situation service federations. Sensor entity simulation
federations subscribe entity overall situation information
from objective situation service federations and publish
entity cognizing situation to cognizing situation service
federations. Communication node federations subscribe
cognizing situation from cognizing situation service
federations according to communication organization
relationship and publish command situation to command
situation service federations. Command node federations
subscribe command situation to themselves from command
situation service federations.
A. Data description standards
Defining data description standards is to achieve data
understanding of between entities and layers in the
simulation. Common method is to define data format and
meaning, that is, PDU (Protocol Data Unit). 27 kinds of PDU
are defined in [4], including entity information/interaction,
battle, logistic support, electromagnetic mission, radio
communication and so on. It has an authority and can use for
reference. We also can define it by ourselves, but
consistency of all the nodes in the system must be ensured. It
is basis to ensure that all kinds of data between entities are
understandable.
B. Data synchronization within layer
When a data layer has several servers, achieving layer
data synchronization and consistency, that is, data storage,
retrieval and distribution, is a technical difficulty. It is also a
common problem in large-scale distributed simulation.
Large-scale data sets and data transmission delay in widearea-wide
makes the efficiency of data transmission to be a
bottleneck.
Traditional synchronization is through consistent data
distribution and formation of unified data copy to implement
resulting in flooding. A non-uniform information distribution
technology is introduced in literature [5]. Data distribution
protocol is divided into the probability protocol, priority
distribution protocol and change sensitivity protocol. Data is
moved by GridFTP service in literature [6]. It is a data
accessing and transmission based on grid environment. It
supports parallel data transmission and allows create
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multiple TCP streams between the data source and
destination. In this method, data can be transmitted in piece.
IV. APPLICATION CASE
Now, we give process of radar detection finding the
target in the surrounding environment in the battle simulation
to indicate the advantages of this method. In the tradition
technology system, various types of targets in the simulation
environment need to give their characteristics and movement
to radars. Then whether the targets are detected is decide by
the radar’s own performance and the detection information is
given. If there are several radars in the simulation, they all
simulate in this way. The interaction relationship between
nodes in the network is irregular mesh. If using the method
in this paper, various types of targets only need to give their
characteristics and movement to objective situation server.
Then the objective situation server coordinates and forms a
unified objective situation. The interaction relationship
between nodes in the network is the star model. Radars
cognize and judge this unified objective environment and
then send the cognizing results to the cognizing situation
servers. Increase or decrease in the number of radar, the
target environment, the increase or decrease of the radar’s
number does not affect the simulation system. It is no need to
change programs. Figure 4 shows simulation system of two
nodes. The node 1 is running a simulation model of aircraft 1,
ship1 and radar1 and node 2 is running a simulation program
of the aircraft 2, ship 2 and radar 2.The output data of
platform class entities in each node form an objective
situation layer on the server, the objective situation of layers
of two nodes are communicated through data sharing
mechanism, resulting in the objective situation layers of two
nodes in exactly the same. They provide the same cognizing
environment to the radar model running on these two nodes.
Figure 3. Ilustration for a Cross-domain Simulation System Constituted
by Two Nodes
V. CONCLUSION
The core idea of battle simulation interaction
organization is to divide the nodes into troop entity nodes,
communication organization nodes and command nodes. The
“Produced / Consumed” data of various types of nodes is
divided into objective situation layer data, cognizing
situation layer data and command situation layer data to
manage. By using “layer” as the interaction center, the layer
synchronization and separation between layers of simulation
situation data are achieved. In general simulation network,
the objective situation server, the cognizing situation layer
and command situation layer are added to maintain various
types of data.
Various types of entities in simulation have no direct data
exchange, but interact indirectly through the situation server.
It can solve the problem which the HLA/RTI technology
cannot solve in multiform nets and has the following
advantages:
1) It is coupled in low level and easy to expand. In
traditional simulation systems, a new simulation entity need
and may be related to occurrence of data dependencies to
contact all the nodes. Using the system architecture provided,
when a new entity joins simulation system, only need to
establish data producing and subscribing relationship within
the system node according to the types of data produced and
subscribed, and the entities that already exist within the
system does not need directly interaction。
2) It greatly reduced the scale of data interaction in the
large-scale distributed simulation. Various types of entities
don’t need data interaction any more, but indirectly through
various situation servers’ data interaction. Supposing there
are m combat platform entity nodes, n sensor entity nodes. In
traditional battle simulation system, the need to establish the
relationship need m× n data interaction, and using this
method, the data interaction reduced to m + n .
3) It is Easy to organize simulation model validation.
When conducting simulation entity model validation, the
input and output information of various entities can be
directly collected and analyzed on the server. The radar
model reliability can be validated through collecting
surrounding target information on the objective situation
server, radar cognizing information on cognizing situation
server. The radar detection model can be calibrated and
validated with the radar performance parametersAuthors and
Affiliations
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