Metron Inc (MP 07 03) Topic BMDO 02-010 Prop # B2-0401

REPLACE WITH PROPOSAL COVER SHEET Reconnecting Subnets in a Decision Architecture Phase II B2-0401

30 January 2003 Revised 18 November 2003 Proposal Submitted to MDA SBIR Program Management Office

by Metron Inc 11911 Freedom Drive, Suite 800 Reston, VA 20190 Ph: 703 787 8700 Fax: 703 787 3518 email: [email protected]

______

Lawrence D. Stone, Chief Operating Officer

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REPLACE WITH PROPOSAL COVER SHEET ABSTRACT: The Decision Architecture (DA) being developed for MDA within project Hercules consists of a large Bayes’ net designed to combine information from multiple sources and systems to help make decisions about the number, kinematic state, and types of targets present during all phases of a ballistic missile attack. The DA will integrate information from a number of disparate subsystems in several locations. During this process it may be useful or necessary for one or more of these subsystems to operate in isolation without sending or receiving information from the rest of the DA. The proposed work will use the results of our phase I work to develop a prototype software system that can (1) connect or reconnect a subnet to rest of the decision architecture in such a manner that information from the subnet is correctly and optimally integrated into the Bayes’ net representing the rest of the DA; (2) dynamically reconfigure the net to account for new targets, and (3) incorporate Gaussian and non-Gaussian, continuous measurement information at leaf nodes. The goal is to develop this prototype software so that it can be integrated into the DA. BENEFITS. A successful completion of this work will improve the Decision Architecture by increasing its modularity (connecting and reconnecting distributed nodes), reducing computational load (dynamic reconfiguring to add new targets as needed), and improving inference by use of continuous measurement data. Since subnets can be maintained at different locations and on different computers and connected or reconnected when desired, the DA will be more robust to communications limitations and failures. The DA will be able to dynamically add and remove subnets from the DA at any time.

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TABLE OF CONTENTS

1 Identification and Significance of the Problem or Opportunity...... 3 2 Phase I Results...... 6 3 Phase II Technical Objective...... 14 4 Phase II Work Plan...... 14 5 Deliverables...... 16 6 Related Work...... 16 7 Relationship with Future Research or Research and Development...... 18 8 Commercialization Strategy...... 18 9 References...... 19 10 Key Personnel...... 19 11 Facilities and Equipment...... 22 12 Consultants and Subcontractors...... 23 13 Prior, Current, or Pending Support of Similar Proposals or Awards...... 23

1 IDENTIFICATION AND SIGNIFICANCE OF THE PROBLEM OR OPPORTUNITY The Decision Architecture (DA) being developed by project Hercules consists of a large Bayes’ net designed to combine information from multiple sensors, sources, and systems to help make decisions about the number, kinematic state, and types of targets present during all phases of a ballistic missile attack. The Decision Architecture will integrate information from a number of disparate subsystems in several locations. This proposal identifies four key opportunities to support the development of the DA:  Continue development of the capability for handling the disconnection and reconnection of subnets across a distributed DA network in a robust manner.  Demonstrate the capability for DA networks to use continuous input variables and to overcome problems identified within the DA that arise from the use of discretized representations of continuous measurements.  Demonstrate these capabilities using the RF-IR handover and midcourse classification portion of the BMD problem as an example. This will include mapping the Blue Team ETOM algorithm onto the DA. This constitutes an important step in integrating Project Hercules algorithms into the broader MDA effort.  Develop methods for representing, within a Bayes’ net, the changing target-to- target associations that can arise from the ETOM algorithm. This will require study of re-configurable Bayes’ nets.

1.1 Reconnecting Subnets During the process of operating the DA it may be useful or necessary for one or more of the many subsystems to operate in isolation without sending or receiving information from the

3 Metron Inc (MP 07 03) Topic BMDO 02-010 Prop # B2-0401 rest of the DA. This isolation may occur because of communications problems, bandwidth limitations, or even processing constraints. When such an isolation occurs, it would be desirable to run a subnet to collect and process the information from the subsystem and then reconnect this subnet to the full DA net when possible. When this reconnection happens, the information or beliefs in the full DA should be automatically updated with the information from the subnet without interrupting the ongoing processing of information by the DA. As an example, processing limitations may require the DA to focus initially on information about the boost phase in order to provide timely advice during that phase of flight. As the missile reaches the midcourse part of its flight, the DA will turn its attention to determining the elements of the payload of the missile, e.g., the number and kinematic state of the reentry vehicle(s), decoys, and associated parts such as the attitude control system. At this point the DA will want to incorporate information from subsystems such as ground-based, X- band radars that have been tracking the missile complex. The information collected by the ground-based radars can be computed and represented in a separate subnet. As midcourse approaches, we want to connect this subnet to the rest of the DA and incorporate its information (in the optimal way) into estimates of the state and payload of the missile.

1.2 Using Continuous Input Variables The Decision Architecture group within MDA’s Project Hercules has identified the use of discrete approximations to continuous input variables as a weakness in the current approach. The discretization is carried out for two main reasons. The first reason is that there are a number of exact results that can be proven for discrete input Bayes’ nets and secondly, it is sometimes easier to characterize poorly understood relationships between variables when those variables are reduced to sets of discrete ranges. The weakness of using discrete representations is apparent on a number of levels. The first and most obvious is that information is lost as soon as the variables are discretized. The second and more significant weakness is that discretization leads to the Bayes’ nets of very high dimension. These high dimension Bayes’ nets cannot be manipulated using the memory resources on current computers, or those anticipated over the next decade. They cannot be processed within the time-frame of a real engagement. A variety of solutions to these problems of processing time and memory requirement have been proposed. All of these solutions approximate the Bayes’ net in some manner. Approximations include the Boyen-Koller approach, the use of Gaussian kernels, the use of Monte Carlo methods (particle filters), and fractionating of the networks. All of these methods answer the problems caused by the initial approximation with further approximations and take us further from the promise of Bayes’ nets to deliver capability not found in alternative approaches. A key element in our proposal is to explore the use of continuous input variables, as a parallel and complementary thread to the work in the DA. We propose to develop likelihood functions to map continuous inputs onto discrete nodes. Where the physics allows for analytic functions we will use analytic functions. Elsewhere we will develop empirical likelihood functions through simulation or numerical approximation.

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1.3 Mapping ETOM into the DA We will need an example Bayes net on which to develop our work, and we propose to use an elaboration of the simple net used in our Phase I work. This net will represent the mid-course classification of targets and the fusion of IR and radar measurements on those targets. As such we will need to represent our ETOM algorithm, for track-to-track correlation, within the Bayes’ net. An additional benefit of this proposal is that it would constitute a definite step toward bringing a Project Hercules Blue Team algorithm fully into the DA and would be undertaken by a team with experience in both DA work and Blue Team algorithm development.

1.4 Exploring the Need for Re-configurable Bayes’ Nets The ability to connect and reconnect subnets is characteristic of a dynamic net structure. There is a further need for the structure of the overall net to be dynamic. In particular, one would like the net to automatically reconfigure itself to account for the number of targets present. Alternatively, one could develop a net structure that is initialized with a large number of target objects, each representing a possible target, and perform inference on this large number by adding a classification state that is effectively “target not present” to the state space of each target object. One could then perform inference on each of the target objects. For the target objects that correspond to targets that are present, we would expect the probability of “target present” to be close to 1. For the rest we would expect the probability of “target present” to be close to 0. The difficulty with this approach is the large computational load that it imposes on the Bayes’ net, particularly since computation time is already a problem without adding this burden. We propose to ameliorate this problem by developing a dynamic net structure that automatically reconfigures the net to add new targets as they are needed. The ETOM algorithm produces solutions for the track-to-track correlation problem. It may be that as new information becomes available the ETOM algorithm indicates a different association between an IR and RF track than the one previously specified. Behavior similar to this will manifest itself in many data fusion algorithms, and it may be efficient to represent this behavior through a re-configurable Bayes’ net. As part of the other work, we propose to study this issue and provide support to the DA community in this regard.

1.5 Summary The development of software for the DA that provides for reconnecting a subnet to the DA, dynamically reconfiguring the net, use of continuous input variables, and mapping of ETOM into the DA will provide the following benefits: Modularity: Subnets can be maintained at different locations and on different computers. Robustness: The DA will be more robust to communications limitations and failures. Dynamic Reconfiguration. Subnets can be dynamically added and removed from the DA at any time. In addition, as the number of targets increases (or decreases) the net will automatically reconfigure itself accordingly.

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Improved Discrimination and Classification. The use of the continuous values of the measurements rather than discrete representations will allow for more accurate inferences by the Bayes’ net, and better classification and inference. Mapping of ETOM Algorithm into the DA. This will constitute a definite step toward bringing a Project Hercules Blue Team algorithm fully into the DA

2 PHASE I RESULTS In the phase I work (see reference [7]) Metron developed an object-oriented implementation of a Bayes’ net in Java that operates over a distributed network of computers. The net was constructed from instantiations of node objects as described in Section 2.2. Using these objects, we built a simple Bayes’ net which represented two sensor systems trying to classify a set of targets. We developed methods (described in Section 2.1 and 2.2) that allow us to connect or reconnect a subnet to the rest of the decision architecture in such a manner that (1) information from the subnet is correctly and optimally integrated into the Bayes’ net representing the rest of the Bayes’ net; (2) information from the Bayes’ net is correctly and optimally integrated into the subnet; (3) integration is performed without interrupting the incorporation of other information being obtained during the integration; (4) integration is performed automatically and autonomously at the time of reconnection without requiring special operations by Bayes’ net. These methods were incorporated into the Phase I Bayes’ net software. Using this software we demonstrated that we can separate a subnet of this net as described in reference [7], run it autonomously using the sensor and other information obtained from its nodes, and then reconnect it to the main net. After operating in this autonomous fashion for a time, we reconnected the root of the subnet to the main net. Using the same rules that would have been followed if the subnet had been connected all along, it performed belief propagation as described above and produced the same results as if the subnet had been connected all along. In doing this, we showed that goals (1) – (4) above were realized and that the concept of disconnecting and reconnecting subnets is sound and feasible. This means that we can use this approach for connecting and reconnecting subnets without losing information. We will obtain the same result upon reconnection as if the subnet was connected throughout the whole evolution of the problem.

2.1 Description of Mathematical Approach Used in Phase I Bayes’ nets have been developed as a way of performing probabilistic reasoning in complicated systems. See references [1] – [6]. The Bayesian approach to reasoning is to compute posterior probability distributions (called beliefs by Pearl in [1]) on the state of the variables of interest. The variables of interest are connected in a network that graphically shows the causal relationships among them. See reference [7] for a description of the Bayes’ nets developed during the phase I work. The variables in the net are represented by nodes and the relationships are indicated by lines connecting the nodes as shown in Figure 1. A line between a

6 Metron Inc (MP 07 03) Topic BMDO 02-010 Prop # B2-0401 parent node U and a child node X means that there is a relationship between the state of node U and the state of node X which is expressed in terms of a transition function, P( x | u )= Pr{ X = x | U = u } . This is the method by which causal relationships are modeled in a Bayes’ net. Reference [5] presents reasons why it is natural to model causal relationships using probabilities and, in particular, transition probabilities and posterior probability distributions. Node U

Node X

Figure 1. Simple Network Representing a Bayes’ Net In reference [1], Pearl describes a method of belief propagation in causal polytrees (singly-connected networks) that prescribes rules for each node of a Bayes’ net to follow. The rules specify that messages in the form of l functions are passed up the network (to parents) and p functions are passed down the network (to children) from a node. The l functions are similar to likelihood functions, and the p functions are similar to posterior distributions. Each node acts like an object (in the computer science sense) and continues to receive and pass messages until no more messages are received. At this point the evidence and belief has been fully propagated and updated throughout the net. In Phase I we built a Bayes’ net that represents a simplified DA using the belief propagation methods of reference [1]. In this net, each node is instantiated as an object in the computer science sense. The object receives l and p messages, and depending on its local data, sends out l and p messages. Each node in the net is an instantiation of this object. Only its local data will change. Each node (object) is designed to operate autonomously using only its local data and the messages passed to it. We implemented the belief propagation methodology of Pearl, reference [1]. We describe this methodology in two steps. First we discuss belief propagation in tree-structured causal networks, i.e., those in which every node (except the root node) has exactly one incoming link as shown in Figure 2(a). We then extend the method to causal polytrees, i.e., singly- connected networks where nodes may have multiple incoming links as in Figure 3.

Belief Propagation in Tree-Structured Causal Networks. Consider the node X in

Figure 3(b), and suppose there are m child nodes Y1,K , Ym as shown but only one parent node U1

. Let x1,K , xn be the possible values of the variable X representing the node X. If the node at X has access to the following two functions, it can compute the posterior distribution on X.

p(x )= Pr{ X= x| U } for x = x1 ,K xn

l (x )= Pr{ (Y1 ,K Ym ) | x} m m =PrY| x = (x ) for x = x ,K x 照i=1{ i } i = 1l i1 n

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message

Data message (a) Data (b) (c)

(d) (e) (f)

Figure 2: Belief propagation in a tree-structured causal network.

U1 U2

Un

X

Y1

Ym Y2

(a) (b)

Figure 3: (a) Fragment of a polytree. (b) Parents and children of a typical node. The function p(x ) is the posterior probability that X= x given all the information contained in the node U and its parents. The function l (x ) is the probability of obtaining the observations (Y1 ,K Ym ) from the children of node X given X= x . From these two functions one can compute the posterior distribution BEL on X by

BEL( x )= Kl ( x ) p ( x ) for x = x1 ,K xn where K is a normalization constant.

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Propagation Mechanism. We now describe how the node X computes the messages that it will pass to its parent node and to its children. Node X receives the message p from its parent node U and the messages l i for i= 1,K m from its children. Node X sends the following messages. 1. Message to Parent: To compute this message, it uses the transition function from node U to X, specifically, P( x | u )= Pr{ X= x| U = u} . From this it computes

lX (u )= l ( x ) P ( x | u ) x

and sends l X to the parent node U. 2. Messages to Children. The message sent to the ith child is

pi(x )= K i p ( x ) l j ( x ) j i

where Ki is a normalization constant. Figure 2 illustrates how this process of computing and passing messages propagates throughout a net. Figure 2(b) shows data (observations) entering the net at two of the nodes. Each of these nodes sends a l message representing the information in the data up to its parent node. This is represented by the red arrows in the figure. In Figure 2(c) the nodes receiving the l messages compute and send p messages down to their children (green arrows) and l messages up to their parents. In Figure 2(d), we see that these messages trigger the sending of p messages (green arrows) to the indicated nodes. Figures 2(e) and 2(f) show the p messages that complete the propagation. At this point no node has received a message that would trigger the sending of another message. The process halts, and belief propagation is complete. Belief Propagation in Causal Polytrees. In a causal polytree, a node can have more than one parent. Suppose that node X has m children, Y1,K , Ym and n parents, U1,K , U n as shown in Figure 3 (b). The node receives the following messages

From parents: pj for j= 1,K n .

From children: l i for i= 1,K m . Node X executes the following three steps. 1. Updates its posterior by computing

BEL( x )= Kl ( x ) p ( x ) for x = x1 ,K xn where

m (x )= ( x ) for x = x ,K x li=1 l i1 n n (x )= P ( x | u ,K , u ) ( u ) p1 nj=1 p j j u1 ,K un

2. Propagates to parent U j for j=1,K , n

lj(u j )= 邋 l ( x ) P ( x | u1 ,K , u n ) p k ( u k ) x uk : k j k j

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3. Propagates to child Yi for i=1,K , m BEL( x ) pi (x ) = K l i (x ) Reference [1] observes that this method of propagating and computing beliefs (posterior distributions) has the following properties. 1. “The local computations and the final belief [posterior] distribution are entirely independent of the control mechanism that activates the individual operations. The operations can be activated by either data driven or goal-driven (e.g., request for evidence) control strategies, by a central clock or at random.” 2. “New information is diffused through the network in a single pass. Instabilities and indefinite relaxations are eliminated by maintaining a two-parameter system ( p and l ) to decouple causal support from diagnostic [observational] support. The time required for completing the diffusion is proportional to the diameter of the network.” Key Observation. Using the two parameter system ( p and l ) for belief computation and propagation allows us to disconnect and reconnect subnets in a natural fashion. Specifically, suppose that the subnet consisting of the node X and all its decedents is disconnected from the rest of the net. There are two cases to consider.

1. If the disconnection occurs at the beginning of the problem, then pX , the p vector for node X is set to have a uniform distribution on the states of X. Since no p messages will be received while this subnode is disconnected, we see from that pX will remain unchanged. From we see that when X receives l messages from its children it will send p messages that are proportional to the likelihoods received. When the subnode is connected to the rest of the net, node X will receive p messages from its parent nodes and then send l messages to its parent nodes. The function l at node X will have in it the cumulated (by multiplication) l functions from all its children. Thus the l messages it sends to its parents will be the same as if it had been connected all along. As the messages pass through the net, the l and p vectors at each node will assume the same values as if the subnode had been connected all along. 2. If the disconnection takes place after the net has been operating for some time, the same process takes place. In this case node X stops receiving messages from and sending messages to its parents. The subnet continues to pass messages based on the data obtained by the subnodes. When the subnet connects again, node X receives the updated p messages from its parents and sends its updated l messages. These messages propagate through the net in their normal fashion until equilibrium is reached. The same equilibrium will be reached as if the subnet had remained connected the whole time.

2.2 Description of Bayes’ Net Software Developed in Phase I Metron developed a simple prototype Bayes’ net software package in Java that operates on a Bayesian network that is distributed over multiple computer systems and performs inferencing and updating using the methodology described above.

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The Bayes’ net was constructed from instantiations of node objects. A node object has the characteristics illustrated in Figure 4 below. For simplicity we show only the case of a causal network.

1. A node object will take in l i messages from its children as shown at the bottom of the figure. These messages are combined by pointwise multiplication to form a composite likelihood function l . The function l (x ) is composed with the transition function P( x | u ) to obtain l X (u ) . Composition in this case means dot product of the vectors.

2. The message pX (u ) from the parent node is composed with P( x | u ) to obtain p(x ) . Then BEL(x) is computed. From BEL, the p messages are computed and sent to the child nodes as shown.

3. The functions l (x ) , p(x ) , and P( x | u1 ,K , un ) along with the list of parent and child nodes for this node form the local data for this object. The software includes a graphical user interface (GUI) with a text area for client/server messages (see Figure 5). It displays the nodes and their causal relationships which are represented by connecting lines. At each node there is a display of the locally maintained network nodes (colored white), and distributed connected nodes (colored yellow). See Figures 6 and 7. Figure 7 shows the subnet represented by the connected node “IRA Distributed” in Figure 6. If a distributed node is disconnected, it is colored magenta (not shown). The network is constructed from a network text file which is read in upon initialization of the client/server module. The network name, node name, node values, evidence or evidence files, and conditional probability matrices are all read in from this file. Once the local network has been constructed, the module determines whether it needs to connect to any distributed nodes on the same network. Every ten seconds the module will attempt to connect/reconnect to any currently unconnected distributed node where a connection is required. The assumption from the software side is that there is a primary server and all the distributed servers know this server’s ip address and listening port number. These are read from a configuration file. The primary server may or may not maintain a section of the network. It works primarily to manage distributed connection requests. When a secondary module needs to connect to a distributed node, it sends a request to the primary server which then sends messages to all known servers maintaining parts of the requested network. The messages in the Bayes’ net are constructed and sent any time new data or new nodes are added into the system. Testing has shown that the message construction and passing is occurring correctly and producing accurate network updates.

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Message to Parent (U) Message from Parent (U)

λX(u) πX(u)

P(|u)λ π P(x|)

λ(x) π(x)

(x )  i ( x ) BEL K i

λ1(x) λ2(x) BEL(x)

BEL( x ) / 2 ( x ) BEL( x ) / 1 ( x )

λ1(x) λ2(x) π1(x) π2(x)

Messages from Children of X Messages to Children of X Figure 4. Schematic of the Node Object X

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Figure 5: Example of Bayes’ Net

Figure 6: Net Showing Remote Nodes Connected

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Figure 7: Remote Node IRA

3 PHASE II TECHNICAL OBJECTIVE The Phase II technical objective is to develop a prototype Bayes’ net software system capable of handling a complex problem similar to the one faced by the DA team and having the following capabilities 1. Operates on a distributed network of computers 2. Connects and reconnects subnets without loss of information 3. Restructures the net to account for new track-to-track associations and to add new target subnets as necessary 4. Accepts Gaussian and non-Gaussian continuous measurements or observations at leaf nodes without discretization. 5. Integrates ETOM algorithm

4 PHASE II WORK PLAN The Phase II work plan consists of the following tasks.

4.1 Task 1: Develop an object oriented implementation of a complex distributed Bayes’ net In this task we will follow the same plan as we did for the phase I demonstration, but we will make the software implementation more general, robust, and capable of handling nets with large numbers of nodes. As in Phase I, the net will be designed to operate in a distributed fashion with subnets located on physically distinct computers. We will take advantage of the experience we have gained in developing the prototype type software in Phase I.

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4.2 Task 2: Regression Test the Bayes’ net software on a DA problem We will work with the DA team to identify a problem on which to test our software. Once this has been identified we will use our Bayes’ net software to represent the identified problem. Using data provided by the DA team we will use our Bayes’ net software to run a case that has been run using the existing software. We will compare our results to determine that they have produced the same result as obtained by DA.

4.3 Task 3: Verify the ability to dynamically connect and reconnect subnets. Using the same example as in 4.2, we will run a number of cases to demonstrate that we can dynamically connect and reconnect subnets and still produce the same answer as if the net had been fully connected throughout the example.

4.4 Task 4: Add the ability to dynamically reconfigure the net The current baseline allows for configuring the Bayes' net in a distributed fashion, allowing updated operation as disconnected subnets reconnect. In order to allow dynamic node creation, the Java Code will be enhanced to include functionality for dynamic object creation. Metron has previously accomplished this implementation in developing the SPEEDES simulation engine (see reference [8]). For this task we will add the capability to dynamically create a new subnet. This will be done in two steps. 1. In the first step, we will develop the capability to tell the net to add a new target. When this happens the net will create the appropriate subnet and connect it to the main Bayes’ net. 2. In this step, we will use inferences from the Bayes’ net itself to determine when to create a new target object and associated subnet.

4.5 Task 5: Add the ability to use continuous measurements or observations without discretizing the inputs This task involves designing the leaf-nodes to use transition functions that are computed analytically. Specifically, the transition matrix is replaced by a likelihood function where the measurements Y can have continuous values. The internal state space of the Bayes’ net still remains discrete with variables such as target type. Returning to equation (1), at a leaf node, the function  will allow the value of Y to be continuous while variable x must still take the discrete values x1,K , xn . This means that  can no longer be represented by a matrix. Instead there must be analytical relationship by which we can calculate

l (xi )= Pr{ Y = y | x i } for i = 1,K , n . This means that we must know the probability (density) function that the observation Y follows when the object’s state is xi . This is typically computed from a physical model of the measurement process plus a probabilistic characterization of the measurement errors. If it is not possible to proceed from a physics-based approach, we will have to estimate the relationship empirically, e.g., find a Gaussian or other probability density function that fits the data.

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Note that any node that is not a leaf node will still be required to have discrete input variables and state variables. Thus, the internal states of the net cannot be continuous. This will, however, provide a more accurate representation of the inferences to be made from continuous observations at leaf nodes.

4.6 Task 6: Improve the Graphical User Interface (GUI). GUI enhancements will permit the user to display nodes as they are dynamically added to the net. As another improvement to the display, we will allow the user to collapse a subnet and display it as a single node. This will be useful for a large net which can become too large to comfortably displayed on one screen,

4.7 Task 7: Develop the above capabilities into a software package that can be run and tested on problems faced by the DA

4.8 Task 8: Demonstrate the capabilities of the system through a mapping of the ETOM algorithm into the DA

4.9 Task 9: Study the need for re-configurable Bayes’ nets in order to represent changing data association results arising from data fusion algorithms in general and ETOM in specific.

4.10Task 10: Write a final report describing the methodology embodied in the software. We will write a final report documenting the methodology and the software used to implement the methodology.

5 DELIVERABLES Metron will provide the following deliverables 1. A copy of the computer code that implements the software developed in tasks 4.1 through 4.9. 2. A user’s manual for the software 3. A final report describing the methodology embodied in the software 4. Technical memoranda and briefings as appropriate 5. Progress reports as requested.

6 RELATED WORK Project Hercules. Dr. Stone and other members of the Metron technical staff have worked for project Hercules in the areas of multiple sensor fusion, discrimination, and improving the handover of Radar and IR tracks to the IR seeker in the kill vehicle. This work has involved applying Bayesian inference and tracking techniques to combine information from multiple sensors and systems to improve discrimination, track matching, and the quality of the Target Object Map to be handed over to the seeker in the kill weapon. Client: MDA, Steve Bravy, (703) 697 6419. This work is ongoing.

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Bayesian Inference Nets for Counter-Terrorism. The Evidence Extraction and Link Discovery program, sponsored by DARPA, uses advanced data mining technology to detect and predict terrorist activities. Metron is the lead contractor in the area of Link Discovery. Bayesian Inference Networks play a critical role in our approach. A fundamental concept in Metron’s approach in the EELD program is that of a Dynamic Terrorist Enterprise Model. Our approach has each candidate enterprise model existing in some well-defined state space. The essence of the Metron link discovery algorithm is to estimate the state of the terrorist enterprise, which results in a probability distribution across all possible states of an enterprise. Bayesian Inference Networks are ideally suited to process the wide variety of intelligence data for this estimation process. In the EELD implementation, the Bayesian Inference Network has a single top node that corresponds to the enterprise state space Because the amount of information from intelligence sources is so enormous, processing all of it through the Bayesian Inference Network is not practical. The Metron approach uses an information-theoretic approach to perform a directed search for information that will most likely reduce the uncertainty in the estimate of the enterprise state, thereby greatly increasing the efficiency of the inference process. Specifically, we search for those sources that maximize the mutual information between the enterprise estimate and information contained in those sources. Client: DARPA, Mr. Ted Senator (703) 696-2231. Work is ongoing. Bayesian Tracking. Bayesian Tracking is closely related to Bayes’ nets. In both cases, the objective is to compute a posterior distribution on the state of the object(s) of interest. Information, particularly observations are converted to likelihood functions, combined with other likelihood functions, and applied to the prior distribution to compute the posterior distribution on the number and state of targets present. In tracking, the states are changing dynamically over time and the updating process is usually performed in a recursive fashion. Since its inception in 1984, Metron has been a leader in the development and application of Bayesian inference and decision theory methods for Naval tactical problems. Dr. Lawrence D. Stone is a co-author of the recent book Bayesian Multiple Target Tracking [9]. This is the first book to describe in detail and advocate the use of discrete Bayesian tracking. Spotlight: Development of the Nodestar nonlinear discrete tracker began at the Naval Research Laboratory in 1988 as part of the Tactical ASW Battle Management System (TABS) and reached its culmination in the multiple-target tracker developed for the Spotlight Advanced Technology Demonstration (ATD). The Spotlight ATD included the development of a multiple- target, nonlinear correlator-tracker (Nodestar) for the Navy’s Integrated Underwater Surveillance System. The Spotlight version of Nodestar was developed under contract to the Naval Research Laboratory. Nodestar was successfully tested as part of the Spotlight ATD test in December 1993, at the Dam Neck Naval Ocean Processing Facility. Integrated ASW. Metron has been a leading proponent and developer of Likelihood Ratio Trackers (LRT) and track-before-detect systems in general. An example is the LRT developed for ONR’s Uncluttered Tactical Picture project which is designed for detection of periscopes by shipboard-mounted radar in the presence of heavy clutter. At present Metron is developing Likelihood Ratio Trackers for multistatic active sonar for both for the Distant Thunder and Enhanced Echo Ranging (EER) systems.

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Client: The Distant Thunder and EER work is being funded by ONR (Dr. Larry Green (703) 696-0807). This work is ongoing. Nodestar TMA. Nodestar TMA is a multi-sensor, multi-target tracker designed to use measurements from all the passive acoustic sensors aboard a submarine. The tracker converts these measurements into likelihood functions in geographical space. The likelihood functions are integrated through a discrete tracking process to produce tracks in geographic space. This approach provides a number of benefits: (1) it provides solutions in geographic coordinates rather than coordinates relative to the array; (2) it is able to continue tracking through own ship maneuvers if measurements continue to be obtained during the maneuvers; (3) it can properly represent the ambiguities in conical bearings; (4) it can account for the possibility of bottom bounce paths if acoustic predictions indicate a bottom bounce is possible; and (4) in cases where range estimates are not available, it can use environmental and threat data to limit the ranges of line of bearing detections. This tracking algorithm has been chosen by ASTO to be the close encounter state estimator (tracker) to be included in the operational version of Advanced Processing Build - 01. This build has already been installed on the U. S. attack submarine, Columbus. It is scheduled to be installed on other Los Angles class submarines in the near future. Client: NAVSEA. Dr. Robert Zarnich, SEA 93-ASTO, (202) 781-1297 is the POC for this work which is ongoing.

7 RELATIONSHIP WITH FUTURE RESEARCH OR RESEARCH AND DEVELOPMENT Significance of Phase II work for Phase III Research and Development. If the technical objectives listed in Section 3 are met, this will form the basis for incorporating the capabilities developed in Phase II into the DA. As the Phase II work progress we will stay in close contact with DA team in an effort to develop our methods and software in a fashion that can be integrated into the DA in a reasonable manner in Phase III.

8 COMMERCIALIZATION STRATEGY Our goal is to incorporate the software or technology developed under the phase II funding into the Project Hercules Decision Architecture. First product to receive technology: The technology developed in phase II will be incorporated into a software system for constructing Bayes’ nets. The goal is to convince MDA to use this software (or technology derived from it) in the Decision Architecture. Customers: The primary customer for this product or technology will be MDA, but the technology or the software could be of interest to anyone creating complex, distributed Bayes nets. Money: We plan to ask MDA for phase III funding to incorporate the software or technology developed in phase II into the DA. Marketing expertise: Metron is well known with MDA’s Project Hercules and we are presently funded by them. We have already developed track-to-track correlation algorithms that are being tested by MDA.

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Competitors: Metron has a number of competitors for this work. Our primary advantage rests in having top level of mathematical talent which we combine with advanced computer science capability to develop this technology and implement it in software. Metron has a long history of acting as a bridge between the research and operational communities. This sets us apart from most companies. Quantitative commercialization results from Phase II One year after start of Phase II – none At completion of Phase II – none After completion of Phase II: We expect $500,000 to $1,000,000 of additional investment by MDA in this software or technology.

9 REFERENCES [1] Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl, published by Morgan Kaufman, San Mateo CA, 1988 [2] Influence Diagrams, Belief Nets, and Decision Analysis edited by R. M. Oliver and James Q. Smith, published by John Wiley, 1988 [3] An Introduction to Bayesian Networks by F. V. Jensen, published by Springer, 1996 [4] Probabilistic Networks and Expert Systems by R. G. Crowell, A. P. Dawid, S. L. Laurizten, and D. J. Spiegelhalter, published by Springer, 1999 [5] Causality: Models, Reasoning, and Inference by Judea Pearl, published by Cambridge University Press, 2000 [6] Bayesian Networks and Decision Graphs by F. V. Jensen, published by Springer, 2001 [7] Reconnecting Subnets in a Decision Architecture, Metron Final Report to the Missile Defense Agency on contract HQ0006-02-C-0031 by Neil Fergusson, 29 November 2002 [8] SPEEDES User’s Guide: The Synchronous Parallel Environment for Emulation and Discrete-Event Simulation. Metron report to The Joint National Test Facility, Schriever AFB, Colorado, 4 October 2001. [9] Bayesian Multiple Target Tracking by L. D. Stone, C. A. Barlow, and T. L. Corwin, published by Artech, 1999

10 KEY PERSONNEL

LAWRENCE D. STONE Chief Operating Officer, Metron Inc. Dr. Stone joined Metron in 1986. His work has included modeling the operational Anti- Submarine Warfare (ASW) effectiveness of nonacoustic sensors, developing tactical decision aids for ASW search and localization, and participating in a National Science Foundation project to apply search theory to oil exploration. He was the technical and project manager for the development of a multiple-target, nonlinear, correlator-tracker, Nodestar, designed for use in the

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Navy’s Integrated Underwater Surveillance System. He continues to perform research in the area of non-linear data fusion and is a coauthor of the 1999 book, Bayesian Multiple Target Tracking, published by Artech House. He has worked for MDA on tracking and discrimination problems since 1999. Dr. Stone is a member of the National Academy of Engineering and a Fellow of the Institute for Operations Research and Management Science. In 1986, he produced the probability maps used by the Columbus America Discovery Group to locate the S.S. Central America which sank in 1857, taking an estimated 400 million dollars of gold coins and bars to the ocean bottom one and one-half miles below. The Operations Research Society of America awarded the Lanchester Prize to Dr. Stone's text, Theory of Optimal Search, as the best work in operations research published in 1975. Dr. Stone was codirector of the 1979 NATO Advance Research Institute on Search Theory and Applications in Faro, Portugal, and coeditor of the conference proceedings, Search Theory and Applications. He has published numerous papers in search theory, taught the subject at the Naval Postgraduate School, and has participated in many search operations. He has also published papers in probability theory and optimization. Dr. Stone worked at Daniel H. Wagner, Associates from 1967 until 1986. He became a Senior Associate in 1970, Vice President in 1974, and Senior Vice President in 1985. He managed their Washington, D.C. office from 1984 to 1985 and their California office from 1981 to 1984. Dr. Stone rendered seven weeks on-scene assistance to the U.S. Navy in the 1974 search for unexploded ordnance in the Suez Canal. He participated in the development of the Coast Guard's computerized search and rescue planning program, CASP. During the 1968 search for the remains of the submarine Scorpion, Dr. Stone provided on-scene analysis assistance for six weeks near the Azores.

Education:

Bachelor of Science, Mathematics, Antioch College, 1964 Master of Science, Mathematics, Purdue University, 1966 Doctor of Philosophy, Mathematics, Purdue University, 1967

Selected Publications: Bayesian Multiple Target Tracking, L. D. Stone, C. A. Barlow, and T. L. Corwin, Artech House, Norwood MA, 1999. "Nodestar: A Nonlinear, Discrete, Multiple-Target Correlator-Tracker: Part 1," (Unclassified) L. D. Stone and T. L. Corwin, Journal of Underwater Acoustics (USN) 45, 525-540 (1995) (Secret-NoForn). Nodestar: A Nonlinear, Discrete, Multiple-Target Correlator-Tracker: Part 2 (U)," (Secret) L. D. Stone and T. L. Corwin, Journal of Underwater Acoustics (USN) 45, 541-548 (1995) (Secret-NoForn). “Search for the SS Central America: Mathematical Treasure Hunting,” L. D. Stone, Interfaces, vol 22, No. 1 pp. 32-54, January-February 1992.

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“Bayesian Estimation of Undiscovered Pool Sizes Using the Discovery Record,” L. D. Stone, Mathematical Geology, Vol. 22, No. 3, pp. 309-332, 1990. Theory of Optimal Search, L. D. Stone, Institute for Operations Research and Management Sciences, Linthicum, MD, Second Edition, 1989. “The Process of Search Planning: Current Approaches and Continuing Problems,” L. D. Stone, Operations Research, Vol. 31, pp. 207-233, March-April 1983. “Constrained Optimization of Functionals with Search Theory Applications,” W. R. Stromquist and L. D. Stone, Mathematics of Operations Research, Vol. 6, No. 4, November 1981. Search Theory and Applications, Edited by K. B. Haley and L. D. Stone, Plenun Press, 1980. Proceedings of the NATO Advanced Research Institute on Search Theory and Applications, Praia Da Rocha, Portugal, 26-30 March 1979. “Necessary and Sufficient Conditions for Optimal Search for Moving Targets,” L. D. Stone, Mathematics of Operations Research, Vol. 4, pp. 431-440, November 1979.

CHRISTOPHER M. BONER Analyst Dr. Boner joined Metron in July of 2001. He helped to develop and implement non- linear Bayesian tracking algorithms for the Distant Thunder and Robust Passive Sonar submarine tracking programs. Both projects required development of multiple-target likelihood ratio trackers in Java and maintenance of a substantial code base. Dr. Boner currently works on the Evidence Extraction and Link Discovery (EELD) DARPA project. He helped to develop and implement a Bayesian tracker for terrorist operations that fuses (potentially false and non-chronological) evidence about subtasks to estimate (via a probability distribution) the operation state/capabilities at any moment in time – past, present or future. Dr. Boner conducted a sensitivity analysis to quantify performance (both present and future state estimation) when noise and sensor reliability parameters are poorly estimated. Later on the EELD project, Dr. Boner led the design of a threat detection system that, given patterns of threat and clutter behavior and massive amounts of structured, relational data, detects, classifies and issues alerts for instances of threat activity. Dr. Boner implemented a Likelihood Ratio Hypothesis Scorer component that employs a Bayesian net to discriminate between threat hypotheses and those that arise from noise and clutter. During the 2003 EELD Technology Integration Experiment, the Metron/Carnegie Melon integrated threat detection and group detection systems dramatically outperformed all other participants. Before joining Metron, Dr. Boner was a professor in the Department of Mathematics and Computer Science at Denison University. He published two research papers during the 2000- 2001 academic year, one on the theory of error-correcting codes and one on finite group theory. Dr Boner has experience programming in Java, C++, Matlab, Maple and Korn/bash shell.

Education: Bachelor of Arts, Mathematics, Bucknell University, 1995 Doctor of Philosophy, Mathematics, University of Virginia, 1999

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11 FACILITIES AND EQUIPMENT Facilities. Metron has its corporate headquarters in Reston, Virginia and an office in San Diego, California. Facility clearance. Metron has a Top Secret facility clearance for performing work on classified DoD contracts. Computer facilities. Metron is committed to taking advantage of state-of-the-art information technology. To this end, Metron has implemented a high-performance multi- protocol Wide Area Network that incorporates powerful computers from a variety of vendors. These include UNIX workstations and servers from Sun, Hewlett-Packard, numerous Pentium class Intel-based machines running Windows 2000, Windows NT and Linux. Metron can develop software for UNIX, Windows NT, and Windows 2000 in environments that include C, C++, FORTRAN, Visual BASIC, and Java. Metron’s internal network includes TCP/IP, fast Ethernet, fiber, and ATM equipment. Metron is dedicated to creating interoperability between similar and dissimilar systems, with more than a terabyte of shared storage accessible from any system on the network. Metron makes use of the communications technologies enabled by our system, with electronic mail, group scheduling, and file sharing via the local network or high- speed remote access. Metron has extensive in-house printing capability, including high-speed laser printers, color laser printers, and a large format HP plotter. Metron also operates a high- bandwidth Internet connection available to all the machines on our network. Technical library. Metron’s expanding technical library contains more than 2000 volumes in the mathematical, physical, and engineering sciences as well as periodicals in applied mathematics and physics. Environmental laws. Metron, Inc. meets all environmental laws and regulations of federal, Virginia, and local Governments.

12 CONSULTANTS AND SUBCONTRACTORS No consultants or subcontractors are proposed for this work.

13 PRIOR, CURRENT, OR PENDING SUPPORT OF SIMILAR PROPOSALS OR AWARDS Metron, Inc. has not submitted a similar proposal to any other Federal Agency. Metron, Inc. does not have any prior, current, or pending support for the proposed work

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