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A Novel Framework for Command and Control of Networked Sensor Systems (Proceedings Paper)

In this paper, we have proposed a highly innovative advanced command and control framework for sensor networks used for future Integrated Fire Control (IFC). The primary goal is to enable and enhance target detection, validation, and mitigation for
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    A Novel Framework for Command and Control of Networked Sensor Systems Genshe Chen* a , Zhi Tian  b , Dan Shen a , Erik Blasch c , and Khanh Pham d a Intelligent Automation, Inc., Rockville, MD 20855;  b Michigan Technology University, Houghton, MI 49931; c AFRL/SNAA, WPAFB, OH 45433; d AFRL/ VSSV, Kirtland AFB, NM 87117   ABSTRACT In this paper, we have proposed a highly innovative advanced command and control framework for sensor networks used for future Integrated Fire Control (IFC). The primary goal is to enable and enhance target detection, validation, and mitigationfor future military operations by graphical game theory and advanced knowledge information fusion infrastructures . The problem is approached by representing distributed sensor and weapon systems as generic warfare resources which must be optimized in order to achieve the operational benefits afforded by enabling a system of systems. This paper addresses the importance of achieving a Network Centric Warfare (NCW) foundation of information superiority—shared, accurate, and timely situational awareness upon which advanced automated management aids for IFC can be built. The approach uses the Data Fusion Information Group (DFIG) Fusion hierarchy of Level 0 through Level 4 to fuse the input data into assessments for the enemy target system threats in a battlespace to which military force is being applied. Compact graph models are employed across all levels of the fusion hierarchy to accomplish integrative data fusion and information flow control, as well as cross-layer sensor management. The functional block at each fusion level will have a set of innovative algorithms that not only exploit the corresponding graph model in a computationally efficient manner, but also permit combined functional experiments across levels by virtue of the unifying graphical model approach. Keywords:  Sensor network, Information fusion, Graph theory, Game theory, and sensor management 1.   INTRODUCTION Future advances in fire control for air/missile defense depend largely on a Network Centric Warfare (NCW) foundation that enables the collaborative use of distributed warfare assets for time-critical operations. Selecting the best shooter from a set of geographically distributed firing units can improve the chances of intercepting the target (by selecting the optimal engagement geometry) and improve the economy of weapon resources (by reducing redundant shots) [1, 2]. For complex threat environments in which sophisticated or significant numbers of aerospace targets exist, Integrated Fire Control (IFC) among automated collaborative weapon systems may be a necessity for victory. The ability to direct distributed warfare resources in a collaborative manner enables major enhancements for tactical fire control. Critical to such a distributed collaborative fire control paradigm are advanced data fusion techniques that intelligently task sensors to make early launch decisions based on shared knowledge of the battle space, including knowing the capabilities and locations of all participating sensors. Advanced data fusion also expands the effective kinematic range of weapons so that interceptors can be launched based on fused tracks formed from distributed sensor measurements, which in turn enable additional operational capabilities such as forward pass and off-board engagement support for guidance relay and target illumination. In traditional warfare control, collocated sensors and weapons are paired for tactical engagements of enemy targets. Such  pairings considerably limit the effective kinematic range of warfare assets which include sensors, weapons, and command and control (C2) systems on board warfare units. Critical to futuristic network-centric warfare are the Defense Transformation and Net-Centric Systems 2007, edited by Raja Suresh,Proc. of SPIE Vol. 6578, 65780L, (2007) · 0277-786X/07/$18 · doi: 10.1117/12.720500Proc. of SPIE Vol. 6578 65780L-1 Downloaded From: on 03/27/2014 Terms of Use:  Shifting to orce Level Thinking NN Cli[t Mmuplu th Mmoplrnith (ClhF[t&) S gl U t    participation and coordination of multiple non-collocated warfare assets in tactical engagements, such that each weapon system is able to develop fire control solutions from information provided by one or more non-organic sensor sources; conduct engagements based on these fire control solutions; and either provide mid-course guidance (in-flight target updates) to the interceptors based on this externally provided information, or in certain cases, have them provided by a warfare unit other than the launching unit. A key enabler is an advanced information fusion capability of participating sensors, weapons, and C2 nodes to share target information in real-time and eliminate correlation errors so the engaging weapon system can utilize the information as if it was produced by its organic sensor(s). Such a shift from local resource management over a single unit to force-level network warfare management is illustrated in Fig. 1. The strategy of data fusion has been applied in threat  predication and situation awareness and the terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a so-called JDL Data Fusion Model, and now called DFIG model [3, 4, 5]. In this model, Level 0, or Sub-Object Data Assessment, is the estimation and prediction of signal/object observable states on the basis of  pixel/signal level data association and characterization. Level 1, or Object Assessment, is the estimation and  prediction of entity states on the basis of observation-to-track association, continuous state estimation (e.g. kinematics) and discrete state estimation (e.g. target type and ID). Level 2 and Level 3 data fusion includes Situation Assessment and Threat Assessment. Level 2 data fusion, situation assessment, attempts to discover the relationships among the objects in the battlespace, events that occur within the battlespace, and the environment. Level 3 data fusion, threat assessment, is the estimation and prediction of effects on situations of planned or estimated/predicted actions by the participants; to include interactions between action plans of multiple players (e.g. assessing susceptibilities and vulnerabilities to estimated/predicted threat actions given one’s own planned actions). One output from Level 3 processing is an assessment of the likely adversary course of actions (COAs). Level 4 data fusion, called process refinement, defines as adaptive data acquisition and processing to support mission objectives. Distributed Resource Management (DRM), as  proposed in this paper, encompasses adaptive data acquisition—effectively sensor resource management, as well as addressing the adaptive management of weapons and warfighting units. Level 5 data fusion, called User Refinement (an element of Knowledge Management): adaptive determination of who queries information and who has access to information (e.g. information operations) and adaptive data retrieved and displayed to support cognitive decision making and actions (e.g. human computer interface) [5]. Other situation models include: Endsley’s model [6] defining Situation Awareness (SA). This model takes a “human-centric” approach to SA. According to the model there are three levels of SA: Level 1 represents the perception of the elements in the environment within a volume of time and space. Level 2 represents the comprehension of their meaning. Level 3 represents the projection of their status in the near future. The significant similarities between Endsley’s model of SA and the JDL model imply that the purpose of Data Fusion is to support SA. The output of Data Fusion is, however, not SA. Instead the purpose of Data Fusion, whether performed completely manually or with the support of computers, is to maintain the situation model and support the decision maker. This paper deals with robust distributed data fusion for net-centric force structures with multiple sensors and platforms. To effect real-time, scalable situational awareness in large-scale, heterogeneous sensor networks, a key methodology explored in this paper is Game theory, which not only offers models for distributed data fusion and allocation of network resources but also provides inherent mechanisms to handle uncertainty. To enhance adaptivity and inter-operability among fusion nodes, an agent-based design methodology is further incorporated into the proposed game-theoretic framework. We are developing a unifying graphical modeling for integrated information fusion across all levels (Levels 0-4) of DoD DFIG Fusion hierarchy. This overall approach contains several distinctive key features.  First  , a mobile agent based computing model and system is proposed for collaborative processing among multiple nodes. Unlike the traditional client/server-based method in which each node sends local information to a  processing center for data fusion, the proposed mobile-agent-based computing approach transmits the partially  processed results and executable code from one node to another. Some of the data processing tasks can be done locally at sensor nodes. Fig. 1: Shifting to force-centric thinking   Proc. of SPIE Vol. 6578 65780L-2 Downloaded From: on 03/27/2014 Terms of Use:  —-S —— Distributed System wgmjwUfl p t   Second,  ontology-based semantic definition of metadata is proposed to handle the sensors as well as the data generated  by them to address cross domain translation capability of messages in sensor native format. Third,  for Object and Situation Refinement, dynamic probabilistic network models are used, where the constituent multi-target tracking and data association problem is treated in a distributed manner by Message-Passing Graphs.   Fourth,  for Wargaming and Threat Refinement, graphical games are exploited and are expressed by equivalent Markov networks, whose correlated equilibrium can be reached via dynamic programming in polynomial time.  Fifth,  a game-theoretic negotiation procedure for distributed sensor management is proposed for autonomously allocating targets to sensors and for self-organizing selection and placement of local fusion nodes in a dynamic manner. Sixth , an ontology-based approach is proposed to assess the impacts of track fusion algorithms on the performances of higher-level data fusion and COA determination in BMD systems. In addition, software will be developed with mobile agent architecture to support integrative fusion across all levels of Net-Centric Warfare (NCW) . 2.   SYSTEM ARCHITERCTURE The envisioned concept for future battlefield operations is based on a network-centric foundation achieved through implementing common processors on distributed units and enabling enhanced information sharing. Fig. 2 provides a context diagram of the distributed units—each containing common processing to develop a Common Operational Picture (COP) of the battlespace. These common processors function collaboratively as a distributed system to produce the COP. One peer, or warfare unit hosting the common processing, is enlarged to show the processor’s interfaces with the unit’s resources. The approach enables each intelligent node to determine the optimum force-level resource management option and gain nodal agreement among distributed units prior to tasking local resources. Shared situation awareness relies on appropriate information architecture to enable data sharing among distributed units. While individual warfare units provide organic capabilities, the real force multiplier is when they are netted together in a mutually supportive role—providing a battle space awareness that is greater than the sum of their individual awareness. The future vision for decentralized and distributed information fusion upholds unit-level command authority. Equipping units with common algorithms to produce identical engagement recommendations at each participating distributed node enables a decentralized, yet force-centric, approach to information fusion. Distributed unit collaboration to achieve shared situation awareness capabilities is achieved through the establishment, maintenance, and management of Peer-to-Peer (P2P) networks that enable an adequate data dissemination capability.   Based on the distributed architecture in Fig. 2, we have been developing a complete suite of solutions to Level 0-4 advanced data fusion for integrated fire control (IFC). A novel  graph-model   based advanced data fusion approach, along with optimal weapon and sensor management algorithms, has been proposed. The problem is approached by representing distributed   sensor and weapon systems as generic warfare resources which must be optimized in order to achieve the operational benefits afforded by enabling a system of systems. As shown in Fig. 3, compact  graph models  are employed across all levels of the fusion hierarchy to accomplish integrative data fusion and information flow control, as well as cross-layer sensor management. While the unifying graphical model are already in place at Intelligent Automation Inc. (IAI), there is still a need for a set of innovative data fusion software algorithms at each fusion level that not only exploit the corresponding graph model in a computationally efficient manner, but also permit combined functional experiments across levels by virtue of the unifying graphical model approach. The current paper fills in this gap to address the importance of achieving a NCW foundation of information superiority—shared, accurate, and timely situational awareness upon which advanced automated management aids for battlefield decision making can be built. Fig. 2 Distributed Data Fusion Architecture Proc. of SPIE Vol. 6578 65780L-3 Downloaded From: on 03/27/2014 Terms of Use:    This paper proposes several key components for distributed data fusion, including computing model, game-theoretic hierarchical entity aggregation, wargaming and fusion point placement and sensor management. 3.   MOBILE AGENT AIDED DISTRIBUTED COMPUTING MODEL AND FUSION SYSTEM Complementing to our graph-model based fusion architecture in Fig. 3, this paper will exploit mobile agents to aid distributed computing, data fusion and information dissemination. The client/server paradigm has been one of the most  popular models adopted in distributed computing [8]. Fig. 4(a) illustrates the client/server-based paradigm. IAI has developed an energy-efficient, fault-tolerant, and bandwidth efficient approach to collaborative signal and information processing among multiple sensor nodes using a mobile-agent-based computing paradigm, which transfers the partially integrated results and executable code from one node to another and the processing can be done locally on the sensor nodes. Fig. 4(b) shows the framework of the mobile agent model. Raw data processing and decision fusion are  performed at the local sensor nodes and across multiple nodes respectively. The proposed mobile-agent-based computing model has many advantages over the traditional client/server model and is very suitable for distributed environment like sensor networks:  Reduced bandwidth requirements . Instead of passing large amounts of raw data over the network through several round trips, only the agent with small size is sent. This is especially important for real-time applications and where the communication is through low-bandwidth wireless connections.  Energy efficiency . Since the total amount of data transmission is reduced, the energy usage can also be reduced as radio transmission is the most energy consuming activity in sensor networks. Scalability . For the client/server-based computing, there will be increased queuing length as the number of clients increases. As a result, it may cause longer processing time and more possible drops at the server side. Unfortunately, in sensor networks, the number of nodes may be hundreds or even thousands. On the other hand, the mobile-agent-based computing may not be affected as the number of nodes increases.  Progressive accuracy . A mobile agent always carries a partially integrated result generated by nodes it already visited. As the mobile agent migrates from node to node, the accuracy of the integrated result is constantly improved assuming the agent follows the path determined based on the information gain. Therefore, the agent can return and terminate its itinerary any time the integration accuracy satisfies the requirement. This feature, on the other hand, also saves power, bandwidth and computation time since unnecessary node visits and agent migrations are avoided. Fig. 3 Agent aided Distributed Multi-Source information fusion   Level 0&1 MTT •   Graphical models for distributed data association •   Hybrid track fusion with automatic switching Level 1, 2 Object & SSA •   Graph modeling by dynamic  probabilistic networks •   Hierarchical entity aggregation Level 3, 4 Wargaming & Resource Management •   Graph modeling by Markov networks •   Markov game with correlated equilibrium Fusion Point Placement and Distributed Sensor Management •   Distributed Game Theoretic approach •   Robust measures mobile agent infrastructure Proc. of SPIE Vol. 6578 65780L-4 Downloaded From: on 03/27/2014 Terms of Use:  I / / - El? rocinq/centr— — / I I SensorNode ct . I - / I o I 0 L) Mobile Agent — o Proteiog Center .4 c5 ensor Node 0 Iy-I *c0 cn IAI has compared the performance of two computing paradigms (a) mobile-agent and (b) client-server. The results are shown in Fig. 5. The execution time measurement is shown in Fig. 5(a). It can be observed that the execution time using  both paradigms grows as the number of nodes increases, but the client/server model grows much faster than the mobile-agent model. This is because as the number of nodes increases, the server has to deal with more connections requested  by the clients at the same time, which increases the execution time. On the other hand, the mobile agent model is less influenced by the number of nodes because there are much fewer connections at any given time for the mobile agent model. The figure also shows that, when the number of sensor nodes is less than 20, the client/server model performs a little better than the mobile agent model. This happens because the mobile agent model needs more connections than the client/server model in order to send and receive mobile agents. Another reason is that the overhead of the mobile agent surpasses the overhead of the client/server model. Therefore, in a network with fewer nodes, the client/server model may have a shorter execution time than that of the mobile agent model. However, if the number of nodes is large, the mobile agent model will perform better. As for the total energy the network consumed shown in Fig. 5(b), mobile-agent computing almost always consumes less energy, because the amount of data transmitted are significantly reduced in the mobile agent computing, thus saving total energy. 4.   OBJECTIVE ASSESSMENT USING DATA ASSOCIATION, TRACK FUSION AND IDENTIFICATION A force-centric tactical network comprises a variety of sensor nodes that collect a combination of measurements,  physical observables and features. These observables are of different quality and importance, since sensors on board various weapon systems can be of different sizes and capability, and may operate remotely or locally. Hybrid data fusion approaches are desired to produce a highly accurate integrated picture of the battle space, while minimizing the tasking resources and communication resources. In this section, we will discuss data association, tracking and identification. 4.1   Distributed data association based on graphical modeling In the multi-target tracking (MTT) context, data association is a fundamental problem and it involves finding the correct correspondence between measurements and target tracks [9]. The multiple hypothesis tracking (MHT) approach [9, 17] is the most successful data association algorithm in the target–dense and clutter–dense environment, but is not a trivial (a) The client/server computing paradigm (b) The mobile agent computing paradigm Fig. 4Different computing models (a) The execution time (b) The energy consumption Fig. 5 Performance Evaluation of Different computing paradigms Proc. of SPIE Vol. 6578 65780L-5 Downloaded From: on 03/27/2014 Terms of Use:
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