An ASW (Anti-Submarine Warfare) Simulator on Smart Phones

Transcription

1 An ASW (Anti-Submarine Warfare) Simulator on Smart Phones Hyunhui Kim, Jemin Lee, Tesup Kim and Kangsun Lee* Department of Computer Engineering, MyongJi University San 38-2 NamDong, YongIn, Kyunggi-Do, , Republic of Korea and Kyu Cheol Cho, Sung Ho Jang, Tae Young Kim, JongSik Lee School of Computer Science and Engineering, Inha University #253, YongHyun-Dong, Nam-Ku, Incheon, , South Korea ABSTRACT 1 As modern weapon systems become complex and expensive, there is an increasing need to predict the effectiveness of a new weapon system before the actual development. In this paper, we present an ASW (Anti Submarine Warfare) simulator to measure the effectiveness of a TAS (Towed Array Sonar) on smart phones. Our simulator is composed of Red and Blue submarine model, Environment model (i.e. sea) and Engagement model to simulate the underwater warfare realistically and to measure the effectiveness of a TAS accordingly. Web services have been developed to send simulation results to smart phone clients. According to the experiments we have conducted, only limited memory and battery have been consumed to simulate weapon systems on smart phones. Our work suggests that a smart phone can be is a viable device to simulate weapon systems, anytime and anywhere. Keywords: Defense Modeling and Simulation, Model Reusability, Modeling Formalism, Standard Interface 1. INTRODUCTION As contemporary weapon systems are armed with hightech sensors and complex controllers, the development cost increases accordingly. However, expecting the effectiveness and ROI (Return On Investment) of the new weapon system is almost impossible in real life. SBA (Simulation-Based Acquisition) [1] aims to help decision makers by providing measurements of performance and effectiveness of the new weapon system before its actual development and deployment. As SBA is popularized in the acquisition process of a new weapon system, more and more people want to have the effectiveness data ready, anytime and anywhere, on various hand-held devices. * Corresponding author: All correspondences should be made to ksl@mju.ac.kr Smart phones [2], such as BlackBerry and iphone, run on complete operating systems software providing a standardized interface and platform for application developers, and, therefore, they have enough capability to be utilized as a mobile simulation device. There have been limited works on smart phone based simulators, since smart phones have limited amount of memory (e.g. 512MB) and time (e.g. Battery Limitation) to simulate complex weapon systems. Most weapon simulators involve many run-time objects with internal simulation engines and utility libraries to solve complex dynamics of weapon systems. Also, weapon simulators often require significant amount of time to manage large interactions among the run-time objects distributed on networks. Without efficient mechanisms to handle time and memory requirements, smart phones cannot be utilized as simulation devices for the weapon systems. In this paper, we present an ASW (Anti Submarine Warfare) simulator to measure the effectiveness of a TAS (Towed Array Sonar) on smart phones. Our ASW simulator is composed of a set of independently executable components - Red/Blue submarine components equipped with a TAS, Environment and Strategy components to simulate underwater warfare, and COP (Common Operation Picture) component to monitor simulations. COP collects simulation results and sends them to smart phone clients using web services [3,4]. According to the experiments we have conducted in this work, the smart phone consumes 20% battery to analyze TAS effectiveness during 1 hour. With the help of thin client and server architecture of ASW simulator, only limited memory space (about 6.83M for running Internet Browser) is required on the smart phone during the simulation. These observations suggest that a smart phone can be a viable device for simulating complex weapon systems, anytime and anyplace. This paper is organized as follows. In Section 2, we consider several research issues related to weapon simulators and smart phones, and then propose our way to construct weapon simulators on smart phones. In Section 3, we introduce our ASW (Anti-Submarine

2 Warfare) simulator that measures the effectiveness of TAS (Towed Array Sonar) in the simulated war environment. Section 4 will conclude this paper with future works to achieve. 2. SMART PHONE BASED WEAPON SIMULATORS Simulating weapon systems on smart phones challenges us to address many research issues. Firstly, we need to have an efficient modeling method to organize a weapon system with sub-systems. With the help of the modeling methodology, we can systematically organize a weapon system with a set of components that can be small enough to be executable on smart phones. Secondly, we need to define plausible software architectures that different mobile/immobile simulation devices can interoperate together Modeling Methodology A modeling methodology guides modelers to organize a system into a set of small components that can be separately executable on different computers and even on smart phones. Discrete Event System Specification (DEVS)] is a formal modeling method to hierarchically describe entities and behaviors of a system. [5] Two types of models are proposed to organize a system hierarchically. An atomic model of DEVS depicts a system as 3 sets (a set of inputs, a set of outputs, a set of states) and 4 functions (external transition function, internal transition function, output function and time advance function). A coupled model consists of a set of atomic models, coupling information among the atomic models, and input/output ports. DEVS has been successfully used in defense modeling and simulation developers and resulted many applications with various computer languages, such as, C++ and Java [6,7]. Fig 1 shows our modeling framework based on DEVS. In order to predict the effectiveness of a weapon system, we have to consider 1) dynamics of the weapon system, 2) environments where the weapon system are deployed, 3) engagement strategies where the weapon system are operated. As illustrated in Fig 1, our framework organizes a weapon simulator with three models: weapon model, environment model and engagement model. Each model can be further composed of components and subcomponents, which are eventually described with 3 states and 4 transition functions. With the help of the modeling framework in Fig 1, we can facilitate reuse of weapon simulators. For example, the environment model of a torpedo can be reused in a missile simulator, even though we can t reuse the torpedo simulator as a whole. Also, our modeling framework helps developers to chop the whole simulator into smallsized components which can be independently executable on different computers and even on smart phones. Fig 1: A Modeling Framework for Weapon Simulators 2.2. Software Architecture Fig 2 lists a set of software architectures [8] with which smart phones can participate in weapon simulations. Fig 2-(a) is a simple thin client/server model. Simulations are all carried out on the server, while the smart phone client simply runs the presentation software. The server can be realized with many computers distributed over the HLA (High Level Architecture) / RTI (Run-Time Infrastructure), a standard distributed simulation framework [9,10]. A web service can be used to deliver simulation results to smart phones. Bridge of the server, in Fig 2-(a), is responsible to communicate with smart phone users via HTTP. Fig 2-(b) is a fat-client model of smart phone based weapon simulators. The client implements simulation objects and interacts with the smart phone users. The server is only responsible for critical simulation functions, such as, time management and message synchronization. Fig 2-(c) removes the distinction between client and server. Components of the weapon system are realized as distributed objects that provide an interface to a set of services. Objects can be distributed across a number of various devices, such as computers and smart phones, and communicate through a universal software bus. The thin-client model is simple to implement smart phone based weapon simulations. Moreover, almost no additional memory spaces are required on the smart phones, except the space for running Internet browser to connect the simulation server. In this paper, we implement the weapon simulator according to the thinclient architecture.

3 Fig 2-(a) Thin-Client Architecture the array (an approximation to the array gain) and DT is the detection threshold. However, the effectiveness of TAS cannot be measured deterministically; It can only be assessed by considering various environment and strategy factors. Fig 3 shows the simulation scenario that our ASW simulator will operate on. A red submarine penetrates to our camp. A blue submarine uses a TAS and patrols AR (Area of Responsibility) to detect the infiltrator (red submarine) as early as possible. Given this scenario, the ASW simulator determines the MOE (Measure of Effectiveness) of TAS with various environmental (e.g wind speed, tide, and sea ambient noise) and strategic factors (circle, zigzag, and rectangle maneuver) [14]. Fig 2-(b) Fat-Client Architecture Fig 3. Scenario Fig 4 shows the architecture of our ASW simulator. The ASW simulator distributes four simulation components: Blue-submarine, Red-submarine, Environment and COP (Common Operation Picture). Smart phone users connect to the simulation server via HTTP using a web service. Fig 2-(c) Distributed object Architecture 3. AN ASW (ANTI-SURMARINE WARFARE) SIMULATOR Submarines use SONAR (SOund Navigation And Ranging) as a means of acoustic location and of measurement of the echo characteristics of "targets" in the water. TAS (Towed Array Sonar) is a sonar array that is towed behind a submarine. TAS offers better resolution and range, compared to a hull mounted sonar, and it covers the blind spot of hull mounted sonar [11-13]. The performance of TAS, SE can be measured by the following equation: SE = SL TL NL + DI DT, where SL is the source level, TL is the transmission loss, NL is the noise level, DI is the directivity index of Fig. 4 Architecture of ASW simulator The Blue and Red submarine have been constructed with our modeling framework as explained in Section 2. Each submarine has the following components: Weapon component simulates the physical parts of a submarine. The Weapon component is further composed of Navigation and Detection subcomponent. Navigation component determines the next position of a submarine according to the

4 patrol maneuver described in the Engagement component. Detection component simulates the TAS behavior. Environment component simulates sea conditions (e.g. tide, wind speed, and see ambient noise level). Engagement component describes strategies to determine maneuver patterns, identify targets and determine weapons to assign Blue and Red submarine are distributed on different computers and send/receive the position of the opponent via RTI [10]. COP monitors simulations and sends the simulation results to smart phones using web services. Fig 5 summarizes the detection distance of TAS by varying a detection threshold. According to Fig 5, the rectangular patrol is slightly superior to other patrol patterns. The blue submarine can detect the infiltrator more quickly, when it patrols the area of responsibility with the rectangle maneuver. Fig 6 shows the change of detection distance when the blue submarine moves with different speeds. As the blue submarine moves faster, the value of detection distance becomes smaller. Among the three patrol patterns, the rectangular patrol is also slightly superior to others. From these experiments, we can recommend submariners to patrol the area of responsibility with the rectangular maneuver when they employ a TAS. Circular and ZigZag patrol disturb the array and eventually reduce the effectiveness of TAS. Smart phone users can start simulations and receive simulation results in textual form. According to our experiments, the smart phone client consumes 20% battery to analyze TAS effectiveness during 1 hour. Also, only 6.83M is required for the smart phone client to receive simulation results on Internet Browser. These observations suggest that a smart phone can a viable device to simulate weapon systems, anywhere and anyplace. Fig 6. Detection Distance vs. Submarine Speed Table 1 summarizes the implementation environment of the ASW simulator in the server and the client side. Table 1. Implementation Environment Server OS Window XP SP3 Main Memory 4GB CPU Core 2 Duo 2.66GHz Programming Language Java Platform.NET Framework 1.1 Scenario DB MySQL DataBase RTI RTI-1.3NGv6-WinNT Client OS Google Android 2.1 Main Memory 256MB CPU 600MHz Internet Brower Webkit Full HTML5 Display 3.7 Inch WVGA (480*854) 16M TFT LCD Battery Continuous Call Time min Continuous Wating Time hour Wi-Fi b/g 4. CONCLUSION Fig 5. Detection Distance vs. Detection Threshold In this paper, we proposed a modeling framework with which we can simulate the effectiveness of weapon systems on smart phones. Our modeling framework organizes a weapon simulator into weapon model(s), environment model(s), and engagement model(s) to improve reusability and to make the reusable components deployable on smart phones. An ASW simulator has been constructed to measure the effectiveness of TAS with various environmental and strategic factors. According to the experiments we have conducted, a submarine using a TAS can detect an enemy more quickly when the submarine patrols the area of responsibility with rectangular maneuver. Clients can start the simulation

5 and have the results ready on smart phones with limited memory and battery. Our work is just a beginning of the smart-phone based defense simulation. In the future, we will continue to explore other types of architecture the fat client and the distributed object architecture and analyze the memory and battery requirements to configure the best settings of smart-phone based simulations. Security issues are one of the important issues that we have not addressed in this paper. We would like to guarantee a high level of security, so that only authorized smart phone users can run weapon simulators at restricted areas. 5. REFERENCES [1] Bipin Chadha and John Welsh, Architecture Conceps for Simulation-Based Acquisition of Complex Systems, Summer Computer Simulation Conference, 2000 [2] Holger Weinhardt, Thomas J. Tierney, James R. Davis, Mobile Commerce A case study of the challenges facing mobile commerce services in the U.S. market, Anthony Brightman University College University of Denver Capstone Project For Master for Applied Science, 2009 [3] Erl T., Service-Oriented Architecture: Concepts, technology and design, Prentice Hall PTR, 2005 [4] Web Services Description Language (WSDL) Version 2.0: RDF Mapping, (2007), [5] Zeigler B.P. and Kim T. G., and Praehofer H., Theory of Modeling and Simulation, 2 nd edition, Academic Press, New York, 2000 [6] Chang Ho Sung, Su-Youn Hong, and Tag Gon Kim, Layered Approach to Development of OO War Game Models Using DEVS Framework, Proceedings of the Summer Computer simulation Conference, 2005 [7] L.F.Perrone, F.P.Wieland, J.Liu, B.G.Lawson, D.M.Nicol, and R.M.Fujimoto, Application of the DEVS framework in Construction Simulation, Proceedings of the Winter Simulation Conference, 2006, pp [8] Sommerville, Software Engineering, 8 th Edition, Addison Wesley, 2007 [9] IEEE Std , IEEE standard for modeling and simulation (M&S) high level architecture (HLA) federate interface specification, 2000 [10] porti, n_page, Latest Stable Version (Portico v0.8) [11] SONAR, from Wikipedia, [12] Towed Array Sonar, from Wikipedia, [13] 6. Robert J. Urick, Principles of Underwater Sound, Mcgraw-Hill Book Company, 1983 [14] Dong-Hun Kim, Ji-Hwan Shin and Chan-ki Kim, On the development of the authoritative representations of submarine for engagement level simulation, Journal of the Korea Society for Simulation, Vol. 16, No. 4, 2007, pp ACKNOWLEDGEMENT This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract UD080042AD, Republic of Korea