Index Terms Underwater Warfare, Defense Modeling and Simulation, Distributed Simulation, HLA/RTI.

Transcription

1 Distributed Simulation of Underwater Warfare based on Standard Synthetic Environment Won K Hwam, Yongho Chung, Sang C Park Abstract this paper presents a framework for underwater warfare simulation in a distributed system that is based on standard synthetic environment The proposed framework adopts SEDRIS (Synthetic Environmental Data Representation and Interchange Specification) to represent the underwater environment in HLA (High Level Architecture) based distributed simulation systems Although SEDRIS provides various merits as an international standard, it has never been easy to utilize SEDRIS in distributed simulation systems because of its broadness and heaviness To relieve the difficulties, we present a practical method to utilize SEDRIS technology for underwater warfare simulation The key idea of the paper is to extract the SEDRIS structure dedicated to the underwater environment which is represented with 4-diemensional grid data The extracted SEDRIS structure can be easily adapted to HLA based distributed simulation systems requiring underwater environment Underwater warfare simulation is implemented along with the proposed framework and it is visualized by using Delta3D TM simulation game engine Index Terms Underwater Warfare, Defense Modeling and Simulation, Distributed Simulation, HLA/RTI I INTRODUCTION Recently, defense modeling and simulation (M&S) has remarkably grown in its importance It is necessary to achieve cost-effectiveness weapon systems development, to examine the synergism of various weapon systems, experiment on the environmental effects of the battlefield to weapon systems, and accomplish the traditional M&S purposes, such as training [1], [2] The phenomenon is caused by the complexity of the modern weapon systems which are highly increased by the development of the scientific technologies in comparison with the weapons` history The battlefield of modern warfare is not the equal to the simple concept of the past, but it rather involves many complicated resources, such as network-centric warfare (NCW) [3] As the complexity of modern warfare raised, M&S is in demand for various purposes However, efforts to build a new simulation system include large-scaled and complex modern warfare resources require tremendous time, cost, and human-power, and existing simulation systems have been confined to single, isolated applications developed solely for single purpose Thus, distributed simulation system is required to achieve various M&S objectives by integration of legacy simulators, and it provides the means to swiftly and economically create a larger, more realistic simulation [4] Modeling and Simulation Coordination Office (M&SCO) is an affiliated organization of US Department of Defense (DoD) to lead DoD M&S standardization and empowering M&S Department of IE, Ajou University, Korea capabilities to support the full spectrum of defense activities and operations They developed and released distributed simulation architecture, named as High Level Architecture (HLA) standardized by IEEE 1516 in order to facilitate interoperability and promote software reusability [5] Run-Time Infrastructure (RTI) is an implementation of HLA and fulfillment of the objectives of the distributed simulation system In order to succeed in the distributed simulation system, interoperability is a critical requirement among the heterogeneous simulators, but it is hampered by the environmental data interchange The environmental data is crucial in the depiction of the synthetic battlefield situations, but the proprietary nature of various environmental data formats that reside in each simulation platform It is a great impediment for the interoperability The solution to this problem is the standard intermediate data format that permits the interchange of unified data among the heterogeneous simulators The Synthetic Environmental Data Representation and Interchange Specification (SEDRIS) provide a standard interchange mechanism for various environmental data This increases the data reuse across diverse simulation platforms [6] SEDRIS addresses and represents all the aspects of synthetic environment, such as terrain, ocean, atmosphere, and space SEDRIS supports a timely and authoritative representation of synthetic environment [7] It bears the consecutive efforts that are dictated by the DoD M&S Master Plan [8] Command and control of the underwater warfare unifies ships, submarines, aircraft, and ground units Thus, the naval underwater system has a full range of environmental factors to represent the battlefield The general operation of underwater warfare is structured by using three forces namely, submarines, surface ships, and air units Figure 1 describes the concept of the future NCW-based anti-submarine operation system It shows the integration of the combat units to accomplish a given mission [9] In terms of the synthetic environment, Figure 2 shows the relationship between data consumers (simulation experts) which is shown in Appendix and data providers (environment experts) The data consumers require environmental data that is standard, reusable, and interoperable Although SEDRIS provides various merits as an international standard, it has never been easy to utilize SEDRIS in distributed simulation systems because of its broadness and heaviness Since both data consumers and data providers have difficulties in dealing with SEDRIS, there is a huge gap between the consumers and the providers, as shown in Figure 2 This serves as the motivation of this paper, to find out an efficient methodology to utilize SEDRIS technology for the distributed simulation of underwater warfare 284

2 Underwater environmental elements consist of various values, such as temperature, salinity, pressure and current direction Since these environmental elements have an effect on the performance of weapon systems, the environmental data need to be carefully provided to simulators through RTI Our objective is to present a practical methodology to bridge the gap between data consumers and data providers The key idea of the paper is to extract the SEDRIS structure dedicated to the underwater environment distance between the source and the underwater objects It can be the most effective means that is available to make submarines track targets and navigate underwater [11] The performance of sonar operations is affected by variations in sound speed Sound travels about 1,500 M/s in seawater and 340 M/s in air However, the speed of sound in seawater is not a constant value, and it varies by temperature, salinity, and water pressure There are two simple equations that are used to calculate the speed of sound in seawater [12] The two equations are for the speed of sound in seawater as a function of temperature, salinity and depth, and those are given by Mackenzie [13] and Coppens [14] Sonar operations must be executed with the due consideration of some oceanic environmental features, such as thermocline, so far (an acronym for Sound Frequency and Ranging) channel and water mass Those features are also affected by temperature [15] Essential environmental properties of the underwater battlefield include temperature and salinity, and those values are dependent on a specific time (1-dimensional) and position (3-dimensional) Fig 1 Anti-Submarine Warfare (Network-Centric The extracted SEDRIS structure can be easily adapted to HLA based distributed simulation systems requiring underwater environment The overall structure of the paper is as follows Section 2 describes the modeling approach to the underwater battlefield Section 3 presents the detailed construction procedure of the synthetic battlefield using SEDRIS which is compatible with HLA/RTI An example of an underwater warfare simulation is demonstrated in Section 4 Finally, concluding remarks are given in Section 5 Altitude Longitude Latitude Time: Winter Time: Autumn Time: Summer Time: Spring Numerical Values of Properties Ex) Salinity II APPROACH TO MODELING THE UNDERWATER BATTLEFIELD For the modeling of the underwater battlefield, it is necessary to identify the environmental elements affecting the performance of weapon systems Radar (an acronym for Radio Detecting and Ranging) is an object-detection system that uses radio waves which do not work in the underwater environment as radio waves hardly penetrate through large volumes of water Thus, the mean, instead of the radar, is sonar (an acronym for Sound Navigation and Ranging) and it detects objects that are under the sea by using sound propagation The sonar is the only way to detect underwater objects and the most important system of the warfare that is equipped by every battle unit, which intends to execute underwater operations [10] There are two types of sonars: passive sonar and active sonar Passive sonar system is basically listening for the sound that is created by objects Passive sonar system is a system that is used to indicate the presence, character, and direction of submarines but it does not give a precise range On the other hand, active sonar emits pulses of sound waves that travel through water, reflect off objects, and return to the receiver By knowing the speed of sound in water and the time for the sound wave to travel to the target and return back, the system can quickly calculate Depth Fig 3 4-Dimensional Grid Structure Time Therefore, the underwater battlefield can be represented with a 4-dimensional grid data The 4-dimensional grid structure denotes an environment that is structured by time dimension, horizontal dimensions (ie latitude and longitude), and vertical dimensions (ie altitude and water depth) Figure 3 describes the environmental structure, and it shows several 3-dimensional grid spaces that are defined by latitude, longitude, and altitude/depth along a lapse of the time dimension The 4-dimensional grid environmental structure is an appropriate solution to represent a wide range of spatial and temporal scales in the numerical modeling of the atmospheric and oceanographic data [16] The structure generates cells that are defined by grid axes of dimensions, and it defines properties of a cell For the underwater warfare simulation, numerical values of salinity and temperature are contained in each cell [17] Practical numeric data for this structure are provided by GDEM (Generalized Digital Environment Model), NRL (Naval Research Laboratory), and etc, and they are available on the web to be downloaded [18] Since data consumers require environmental data that is 285

3 standard, reusable, and interoperable, it is necessary to represent the 4-dimensional data structure by using SEDRIS technologies Before the explanation of adopting SEDRIS, we provide a brief introduction on SEDRIS The detailed technical and conceptual resources of SEDRIS can be found in the web pages of SEDRIS, SEDRIS was initiated to achieve two core objectives that are (1) representation and (2) interchange synthetic environmental data SEDRIS structures are with five major components: (1) Data Representation Model (DRM); (2) Environmental Data Coding Specification (EDCS); (3) Spatial Reference Model (SRM); (4) SEDRIS Interface Specification (or Application Programming Interface (API)); and (5) SEDRIS Transmittal Format (STF) SEDRIS represents synthetic environment with meaning and structure that is divided by DRM, EDCS, and SRM They are the core components of SEDRIS and they make SEDRIS to obtain the first objective, representation Understanding each core part of SEDRIS is important to apply Firstly, DRM provides the syntax and the structural semantics to construct a frame for the synthetic environment Secondly, EDCS unifies the characterization of the environmental objects, regardless of what they represent EDCS describes the data attributes with standardized words of EDCS and it provides a mechanism to identify and label the attributes of the environmental objects that employing nine of the EDCS dictionaries that are explained in Table 1 Thirdly, SRM includes all the coordinate systems, and it provides converting the coordination mechanism for the correct representation of the positional information Simulators employ various coordinates systems that are tailored to its purposes, ranges, and directions The potential problem is that an object may be misplaced on a synthetic battlefield, when the simulators are linked to a middleware In order to secure another objective for the interchange synthetic environmental data, STF and API components are available STF is a SEDRIS data file format, and it includes the information for the description of a synthetic environment which is produced by a combination of DRM, EDCS, and SRM The API allows the developers to access and generate STF with DRM, EDCS, Table 1 EDCS Dictionary Classification Dictionary Name Dictionary Description Example Classification (EC) Environmental Object Classification Building, River, Port Attribute (EA) Attribute Value Characteristic (EV) Environmental Object Attribute Classification Attribute Value Spec Classification Height, Depth Data Type, such as Integer, Index, Real Attribute Enumerants (EE) Enumerated Attribute Classification RGB Unit (EU) Attribute Measure Unit Classification Meter, Celsius Unit Equivalence Class (EQ) Attribute Measure Type Classification Volume, Length Unit Scale (ES) Scale Unit Type Classification Kilo, Mili, Nano Organizational Schema (EO) Group (EG) Environmental Structure Type Classification Atmosphere, Ocean, EC and EA Information for EO Universe and SRM [19] Among the SEDRIS components, DRM provides the syntax and the structural semantics To describe a specific environment with SEDRIS, the first thing to be done is to identify the DRM structure which is appropriate to the target environment Once a DRM structure is identified, then we can simply construct a SEDRIS file by filling the DRM structure with proper values The key idea of the paper is to extract the DRM structure dedicated to the underwater environment which is represented with 4-diemensional grid data We call the dedicated DRM structure, an UW-Structure The UW-Structure is specialized to the representation of underwater environment The extracted UW-structure can be easily adapted to HLA based distributed simulation systems requiring underwater environment Figure 4 shows the proposed framework to bridge the gap between data consumers and data providers which is shown in Appendix The UW-Structure enables the automatic generation of a STF (SEDRIS file), and the generated STF can be provided to the data consumers Since STF is an international standard for reusable and interoperable environmental data, it satisfies the requirements of data consumers III DISTRIBUTED SIMULATION OF UNDERWATER WARFARE BASED ON STANDARD SYNTHETIC BATTLEFIELD As mentioned in the previous sections, reflecting environmental effects is necessary for underwater warfare simulation, and SEDRIS is an appropriate method to achieve objectives that represent and interchange the underwater environmental data In order to generate the SEDRIS file (STF) of underwater environment, UW-Structure is required to be defined The UW-Structure begins from the Transmittal Root class It has many classes which can be categorized into metadata, model library, and environment Classes that related to the metadata determine the STF data quality, summary, generation date, and identification, such as publisher, legal and security constraints, and keywords of data Classes for the model library describe the models with - 286

4 color, image, symbol, sound, polygon, and so on Model library avoids any duplicate model to STF Using a model from the model library is to represent the same model repeatedly in simulation Hierarchy of the classes for the environment is constructed by using the Environment Root(ER) class that contains SRM information The ER class branched Geometry Hierarchy (GH) and Spatial Extent (SE) classes out, and the SE class placed under the ER class indicates the spatial extent of geometry representation There are various geometric types that are inherited from the GH class, and the appropriate GH class for the underwater battlefield structure is the Time Related Geometry (TRG) The TRG class is composed of pairs of Property Grid Hook Point (PGHP) class and Time Constraints Data (TCD) linked class to the PGHP class One PGHP class includes one Property Grid (PG) class The PG class defines the spatial information of the environmental data The PGHP class is used for connecting a PG class and a TCD class The TCD class defines the corresponding time to PG data of linked PGHP class A PG class is able to represent from one dimension to three dimensions by spatial axes, and it contains a cell grid structural data table that is framed by the axes The numeric values of the environmental properties are inserted into the table of the PG class The single environmental property is defined by using Table Property Description (TPD) class under the PG class A TPD class contains property specification, such as value unit, scale, and EDCS code Figure 5 and Figure 6 are shown for the representation of UW-Structure which is explained above [20], [21] The generation of standard synthetic environment is the insertion of numeric values of the source environmental data into UW-Structure Next procedure explains the process of insertion of data with regard to the axes and properties in the sub-diagram structure Repeat (++LatC) Until (LatC=Latitude Size) (1) LonC = Initial index; //Longitude dimension counter Repeat (++LonC) Until (LonC=Longitude Size) (1) DptC = Initial index; //Depth dimension counter Repeat (++DptC) Until (DptC=Depth Size) (1) PrtC = Initial index; //Property Counter Repeat (++PrtC) Until (PrtC=Property Size) (1) Set Source Data at (TC, LatC, LonC, DptC, PrtC) to DataTable[PrtC]; (9) Set DataTable to PG; Step 3, Output: TRG; Environment Root TRG N: Number of Time Dimension Values Spatial Extent TCD: 1 PGHP: 1 TCD: N PGHP: N PG: 1 Fig 5 Sub-class structure of time related geometry in UW-Structure PG //Input: Environmental data of time, latitude, longitude, depth axes and properties //Output: TRG class with structured DRM classes along UW-Structure Step 1, Initialize: CreateDRMClasses (TRG, PGHP, TCD, PG, Axes, TPD); InsertEDCS&SRMCode (TRG, PGHP, TCD, PG, Axes, TPD); Step 2, Generate SEDRIS environment format: //21 Construct spatial structure TC = Initial index; //Time dimension counter Repeat (++TC) Until (TC=Time Size) (1) TCDInsertTimeValue (Time[TC]); (2) TRGAddChild (PGHP); (3) TCDLinkTo (PGHP); (4) PGHPAddChild (PG); (5) PGAddChild (TPDs); (6) InsertAxisValues (Lat, Lon, Dpt); (7) PGAddChild(Axis classes of Lat, Lon, and Dpt); //22 Insert data values into the structure (8) LatC = Initial index; //Latitude dimension counter M: Number of Property Value Types Classification Data TPD: 1 TPD: M Axis: 1 In the procedure, environmental data are input parameters, and the function creates UW-Structure and inserts the environmental data into the structure As TC is increased with the time lapse, PGHP classes are attached under TRG class Each axis class has its own dimension range and value, and Axis: 2 Axis: 3 Fig 6 Sub-class structure of property grid in UW-Structure 287

5 three axes structure grid-environment cells The number of properties of a cell is equal to the data table count, and a TPD class A data table shares the index counter to make relationship between the meanings and values After the procedure is completed, the output is a TRG class that has all relevant DRM classes with values and codes in structure under itself In order to apply the generated standard environment to construct a synthetic battlefield, the process starts from reading a STF file Environmental data of the STF are stored in the sub-diagram structure of the DRM classes Therefore, it is required to access a TRG class of the environment root and it contains environmental data Accessing process starts from the matching of the time data of the synthetic battlefield with a TCD class, and it finds out the PGHP class that is linked to the TCD class As explained earlier the sub-structure of a PGHP class, a PGHP class stores environmental data of a given time in the PG class A PG class contains numeric values in the data tables, and it has children classes to structure and define data tables Spatial information of the synthetic battlefield searches the corresponding data of the axis classes of the PG class that are key values to identify grid cells of a data table Figure 7 explains how to identify each cell of a data table in a sub-structure of the PG class which is shown in Appendix It shows the abstract structure of the time and its axes are the key values to access a cell Given below is a procedure to obtain the environmental data from STF for the synthetic battlefield Input data of the procedure is the TRG class, and the Output data are sets of environmental data It shows access to PGHP class, to get time indices (TC), and sub-classes of PGHP, which are directed by the given TC PG class under PGHP class provides data tables and axis classes to get the spatial extent A data table stores the various numeric values in a single array Thus, there is a requirement to arrange these values onto a structured data set In order to make a structured data set, a data table is pointed from the beginning to the end Each pointed data are located into a data set where the place is directed by key values that are calculated from the composition of spatial indices The number of data sets is equal to the count of the properties and this is a determinant of the number of data tables When the structured data sets are available, environmental data can be referred by time and spatial indices //Input: TRG class with structured DRM classes along UW-Structure //Output: Environmental data sets Step 1, Initialize: GetDRMClass (TRG); Step 2, Extract Environmental Data: //21 Obtain the hierarchy of DRM classes of TRG TC = Initial index; //PGHP counter Repeat (++TC) Until (TC=Total count of PGHP classes) (1) GetChildClass(PG); (2) 1st Axis = PGGetChildClass(FirstAxis); (3) 2nd Axis = PGGetChildClass(SecondAxis); (4) 3rd Axis = PGGetChildClass(ThirdAxis); (5) DataTables = PGGetDataTables(); //22 Arrange Data Tables Data on Structured Data Sets TC = Initial index; Repeat (++TC) Until (TC=Total count of PGHP classes) (1) 1stC = Initial index; //1st Axis counter; (2) 2ndC = Initial index; //2nd Axis counter; (3) 3rdC = Initial index; //3rd Axis counter; (4) PrtC = Initial index; //Property counter; Repeat (++PrtC) Until (PrtC=Total count of data tables) (1) DTC = Initial index; //Elements counter of a data table Repeat (++DTC) Until (DTC=Total count of elements of a data table) (1) Insert data of data table at (DTC) into data set at (TC, 1stC, 2ndC, 3rdC, PrtC); (2) 3rdC++; If (3rdC = Total count of 3rd Axis elements) (3) 3rdC = Initial index; (4) 2ndC++; If (2ndC = Total count of 2nd Axis elements) (5) 2ndC = Initial index; (6) 1stC++; If (1stC = Total count of 1st Axis elements) (7) 1stC = Initial index; Step 3, Output: Data Sets; To verify the two proposed procedures, we implemented and tested for a sample environmental data The sample that is formatted as NetCDF contains numerical ocean data of salinity and temperature, and it is structured from 127 to 135 longitudes, from 35 to 42 latitudes, from 0 to 1500 meter depth and from January to December Figure 8 is visualization of the sample at 0 meter in March using Ferret in Linux Figure 9 presents the results of the procedures Figure 9(a) is visualization of the STF data using OSG (An Acronym for Open Scene Graph), and (b) is visualization of exported environmental data in NetCDF format from the STF Both Figure 9 (a) and (b) are for the same depth and time point with Figure 8 By the comparison of the results, Figure 8 is equivalent to Figure 9, and data of the rest points of the sample were also stored in the STF Therefore, the generated STF contains all data of the sample, and the two procedures have been proof to be valid Underwater operations are sensitive to environmental conditions So, in order to acquire reliable results, the synthetic battlefield needs to affect how battle objects may behave in the simulation Hence, underwater warfare simulator has to model the characteristics of battle objects, such as exploration range, detection probability, attack accuracy, and lethality, are able to increase or decrease depending on the synthetic battlefield conditions In order to model the effects, the battle simulator should possess synthetic battlefield data However, the environmental data are usually heavy and broad to manage Therefore, an application is necessary to manage environmental data and provide the battle simulator requested battlefield data Before the explanation for supply of synthetic battlefield data in HLA/RTI system, we need to understand basic characteristics of the HLA/RTI based distributed simulation system 288

6 Fig 8 Visualization of the sample data The HLA/RTI based distributed system is one of the indirect communication systems that follows P-S (Publish-Subscribe) paradigm and this is designated by a server and clients, for distributed event-based systems In the system, clients are assumed as publishers and/or subscribers, publishers publish structured events to an event service and subscribers express interests on particular events through subscriptions [22] In the simulation system that is based on HLA/RTI, a client is called as a federate, and federates publish/subscribe information to/from the RTI server The whole simulation system is termed as federation This specifies, in advance the start of a simulation, a set of federate applications and a common Federation Object Model (FOM) (a) Using OSG (b) Exported Data in NetCDF Format Fig 9 Visualization of the Generated STF Data FOM is a specification that defines the information which is exchanged at runtime to achieve the given objectives of federation It includes communication detail of federates, such as object/interaction classes which are ways of communication among federates In order to communicate with the RTI server, a federate is indispensable to have an interface This is referred to as Simulation Object Model (SOM), and SOM contains information on what its federate is going to publish and/or subscribe data of the classes that are defined in FOM [23] In HLA/RTI based distributed system, the application connects RTI server as a federate that environment federate subscribes synthetic battlefield information and publishes the pertinent environmental data The battle simulator federate publishes the synthetic battlefield information and it subscribes environmental data of the requested battlefield which is published by the environment federate It uses the subscribed data to increase or decrease the characteristics of battle objects that are affected by the environmental effects According to the variation, the results of underwater warfare simulation are changeable In order to interchange the environmental data, the environment federate contains an environmental data server to search the appropriate environmental data of temperature and salinity for the subscribed synthetic battlefield request information The data server has environmental data formed in STFs which are converted from other sources It searches and provides the requested environmental data to the environment federate, and then, the federate publishes the data This work procedure of the environment federate is shown at Figure 10 The battle simulator subscribes the environmental data that are published by the environment federate, and calculates the speed of sound propagation The calculated results are employed to obtain the probabilities of the detection among 289

7 the simulation entities Sonar detection probability is able to be calculated by using functions of performance prediction of active and passive sonar The input parameters of those functions are sonar power, transmission loss, target strength, noise level, direction, detection threshold, and so on, and transmission loss is the only parameter that is affected by environmental situations among them The speed of sound propagation is used to calculate transmission losses by sound transmission models, such as Range-dependent Acoustic Model (RAM) and Bellhop The battle simulator includes these models and sonar performance functions to describe reasonable and reliable sonar operations Figure 10 and 11 are shown in Appendix experiments of the new weapon system, before investing on the resources So, we can find out the problems and modify in advance Consequently, M&S reduces costs, time, and efforts to develop and deploy weapon systems M&S for underwater warfare is devised for the evaluation of weapon systems, such as submarine, torpedo, decoy, and sonar systems, by the representation of engagements by using weapon system models on synthetic battlefield In underwater warfare situations, the weapon systems are very dependent upon the IV ILLUSTRATION WITH DELTA3D The proposed framework for the underwater warfare simulation system was implemented with an example which is a simple engagement scenario of two submarines In order to implement the scenario, battle simulator includes the battle entities, such as submarines and torpedoes, and information of the battlefield Battle simulator publishes the spatial information of the battlefield in order to make the environment federate publish environmental data of the battlefield The environmental data are subscribed by battle simulator, and it influences the range and probability of detection of battle entities Figure 11 describes the synthetic battlefield structure of the example scenario As shown as Figure 11, the synthetic battlefield is constructed on the environment factors to calculate speed of the sound propagation The result values of the speed of the sound propagation are used to compute the sonar performance with the characteristics of battle entities Battle simulator was implemented by using Delta3D TM and C++ Delta3D TM is an open source game and simulation engine, and it is still under development at the Modeling, Virtual Environments, and Simulation (MOVES) institute of the naval postgraduate school in US Delta3D TM is an open source API and provides flexible interface to include various APIs It integrates several open source libraries to achieve the objectives of a simulation game engine, such as Open Scene Graph (OSG) to render 3-D graphics, Open Dynamics Engine (ODE) to simulate rigid body dynamics, Open Audio Library (OpenAL) to handle audio, Character Animation Library 3D (Cal3D) to animate characters [24] Delta3D TM is a sufficient tool to develop simulations for underwater warfare Battle simulator manages the example simulation, and the graphical features are shown in Figure 12 V CONCLUDING REMARKS Defense M&S is a key to achieve several future warfare paradigms that are about minimum waste and maximum effects By applying M&S for the development and deployment of weapon systems is efficient and it is essential to verify the objective, capacity, durability, effectiveness, productivity, and life cycle of weapon systems M&S allows (a) Launching a Torpedo to Enemy Submarine (b) A Torpedo Navigating to Enemy Submarine (c) An Enemy Submarine Damaged by a Torpedo Fig 12 Underwater Warfare Simulation Implemented by Delta 3D environmental information of the battlefield Therefore, interaction between the systems and battlefield must reflect on the simulation In order to describe the interaction in HLA/RTI based distributed simulation system, environment federate is taken place to provide environmental data to the battle simulator In order to generate synthetic battlefield, adopting SEDRIS is essential to promote reusability and interoperability Although SEDRIS provides various merits as an international standard, it has never been easy to utilize SEDRIS in distributed simulation systems because of its broadness and heaviness To relieve the 290

8 difficulties, we present a practical method to utilize SEDRIS technology for underwater warfare simulation The key idea of the paper is to extract the DRM structure dedicated to the underwater environment which is represented with 4-diemensional grid data We call the dedicated DRM structure, an UW-Structure The UW-Structure is specialized to the representation of underwater environment The UW-Structure is specialized to the representation of underwater environment, and bridges the gap between data consumers and data providers The UW-Structure enables the automatic generation of a STF (SEDRIS file), and the generated STF can be provided to the data consumers Since STF is an international standard for reusable and interoperable environmental data, it satisfies the requirements of data consumers ACKNOWLEDGMENT This work was supported by the Defense Acquisition Program Administration under the Contract No UD100009DD and the Agency for Defense Development under the Contract No UD110006MD REFERENCES [1] J F Keane, R R Lutz, S E Myers, and J E Coolahan, "An architecture for simulation based acquisition", Johns Hopkins APL Technical Digest, vol 21(3), pp , 2000 [2] S C Park, Y Kwon, K Seong, and J J Pyun, "Simulation framework for small scale engagement", Computers & Industrial Engineering, vol 59, pp , 2010 [3] K Lee and J Wang, "Combined simulation for combat effectiveness analysis of land weapon systems", Defense Science and Technology Plus, vol 63, pp 4-8, 2008 [4] Australian Defense Simulation Office (ADSO), "Distributed simulation guide", Department of Defense, Canberra, Australia, 2004 [5] M&SCO, "Description of M&SCO", 2012 URL: [6] M Welch, "SEDRIS as an interchange medium", SEDRIS Organization, 1998 [7] P Foley, F Mamaghani, and P Birkel, "The SEDRIS Development Project", SEDRIS Organization, 1998 [8] DMSO, "Modeling and simulation master plan", DoD P, USA, 1995 [9] C E Jr Mundy and F B II Kelso, "Naval doctrine publication 1: Naval warfare Department of the navy", Washington, D C, USA, 1994 [10] P H Brady and D McCormick, "Undersea warfare division: A message from the naval undersea warfare center", National defense industrial association, vol 24, pp 1-10, 2008 [11] U S Navy, "Anti -submarine warfare: concept of operations for the 21st century", Department of the navy, USA, 2005 [12] Discovery of sound in the sea (DOSITS), "How fast does sound travel?", University of Rhode Island, 2012 URL: [13] KV Mackenzie, "Nine-term equation for the sound speed in the oceans", Journal of the Acoustical Society of America, vol 70(3), pp , 1981 [14] AB Coppens, "Simple equations for the speed of sound in Neptunian waters", Journal of the Acoustical Society of America, vol 69(3), pp , 1981 [15] D L Bradley and R Stern, "Underwater sound and the marine mammal acoustic environment: A guide to fundamental principles", Marine Mammal Commission, Maryland, USA, 2008 [16] Unidata, "What is net CDF? Unidata Software, 2011 URL: [17] Q Q Shao, K Rong,, W W Ma, and Z Q Chen, "Generating time-series grid data of sea surface temperature from isotherms in the Northwestern Pacific Ocean using coupled interpolation", Deep-Sea research I, vol 54, pp , 2007 [18] F L Bub, "The status of ocean modeling at the naval oceanographic office (NAVOCEANO)", Ocean Modeling Division, 2011 [19] F Mamaghani, "An introduction to SEDRIS", 2008 URL: [20] L Hembree, R Cox, and V Pastor, "A SEDRIS representation of atmospheric data", Euro Simulation Interoperability Workshop, 2001 [21] W K Hwam, Y Chung, Y Kwon, and S C Park, "Conversion the time dependent grid data of NetCDF to SEDRIS Transmittal Format", Proceedings of the 5th International Conference on IT & Multimedia, 2011 [22] G Coulouris, J Dollimore, T Kindberg, and G Blair, "Distributed systems: Concepts and design, fifth ed", Addison-Wesley, USA, 2012 [23] IEEE Std 1516TM, "IEEE Standard for Modeling and Simulation (M&S): High Level Architecture (HLA) - Framework and rules, 2010 Revised Ed", IEEE Computer Society, New York, USA, 2010 [24] P McDowell, R Darken, J Sullivan, and E Johnson, "Delta3D: A complete open source game and simulation engine for building military training systems", The Society for Modeling and Simulation International, vol 3(3), pp , 2006 AUTHOR PROFILE Won K Hwam received a bachelor degree in industrial and information system engineering, Ajou University, Korea, 2011 He is now a graduate student in industrial engineering, Ajou University, Korea, and he is a member of modeling and simulation laboratory, which is affiliation of department of industrial engineering, Ajou University He is interested in distributed simulation system, synthetic environment, and underwater warfare Yongho Chung received a bachelor degree in industrial and information system engineering, Ajou University, Korea, 2011 He is now a graduate student in industrial engineering, Ajou University, Korea, and he is a member of modeling and simulation laboratory, which is affiliation of department of industrial engineering, Ajou University He is interested in kinetic modeling, and mesh generation Sang C Park was granted his bachelor (1994), master (1996) and PhD (2000) degrees in industrial engineering, Korea Advanced Institute of Science and Technology (KAIST) After his doctor s course, he had been a senior researcher of Cubictek, Korea, for 2 years from 2000 In 2002, he 291

9 moved into DaimlerChrysler and took a srole of research specialist, ITM Dept, for 3 years Currently, he is an associate professor in Dept of industrial and information systems engineering, Ajou University, Korea, since 2004 He is interested in modeling and simulation (M&S), combat simulation for Defense, digital manufacturing system, computer graphics and computational geometry and sculptured surface modeling and NC machining Knowledge on Environment Environmental Factors Salinity Temperature Tidal Current Data Providers Environmental Data Provide Numerical and Raw Data Atmosphere Terrain APPENDIX Gap Approach to Resolve the Gap: SEDRIS: Intermediate Environmental Data Format Urban Ocean Space Knowledge on Simulation Simulation Systems Torpedo Simulation Submarine Simulation Underwater Simulation Data Consumers Synthetic Environment Require 1 The Compliance of Environmental Standard Format 2 Reusability 3 Interoperability Fig 2 Gap between Data Consumers and Data Providers Environmental Factors Salinity Temperature Data Providers Environmental Data Provide Numerical and Raw Data of Oceanology Knowledge Integration DRM SRM EDCS SEDRIS SEDRIS Components 4-Dimensional Grid Structure for DRM Environmental Properties for EDCS Generate SEDRIS Synthetic Underwater Environment Target Simulation System Underwater Warfare Simulation Data Consumers Synthetic Environment Require Standard Synthetic Underwater Environment: Reusable and Interoperable Fig 4 Gap removal by using the UW-Structure 292

10 Time First Axis Second Axis Third Axis First Axis Value 1 First Axis Value I Second Axis Value 1 Second Axis Value J Third Axis Value 1 Third Axis Value K A cell A cell I: Number of First Axis Values J: Number of Second Axis Values K: Number of Axis Values Fig 7 Identifying Each Cell of a Data Table in a Sub-Structure of PG Class NetCDF GriB ASCII type Any Environmental Data Format Import Converter: to STF Export SEDRIS Data Server Provide Search Data of every point of required grid area Environment Federate From start point to end point of required grid area Provide Request HLA/RTI: Interchange Data Among Federates Fig 10 Environmental Data Interchange Process via HLA/RTI Fig 11 Synthetic Battlefield Structure for Underwater Warfare 293