©2001 CRC Press LLC smaller barriers and slow-go regions) will extend the top-down analysis process. At each successive level of refinement, a selection metric, sensitive to progressively more local path evaluation constraints, is applied to the candidate path sets. The path refinement process terminates when one or more candidate paths have been generated that satisfy all path evaluation constraints. Individual paths are then rank- ordered against selected evaluation metrics. While traditional path development algorithms generate plans based on brute force optimization by minimizing “path resistance” or other similar metric, the hierarchical constraint-satisfaction-based approach just outlined emulates a more human-like approach to path development. Rather than using simple, single level-of-abstraction evaluation metrics (path resistance minimization), the proposed approach supports more powerful reasoning, including concatenated metrics (e.g., “maximal conceal- ment from one or more vantage points” plus “minimal travel time to a goal state”). A path that meets both of these requirements might consist of a set of road segments not visible from specified vantage points, as well as high mobility off-road path segments for those sections of the roadway that are visible from those vantage points. Hierarchical constraint-based reasoning captures the character of human problem-solving approaches, achieving the spectrum from global to more local subgoals, producing intuitively satisfying solutions. In addition, top-down, recursive refinement tends to be more efficient than approaches that attempt to directly generate high-resolution solutions. 18.7.2 Detailed Example This section uses a detailed example of the top-down path-planning process to illustrate the potential benefits of the integrated semantic and spatial database discussed in Section 18.6. Because the database provides both natural and efficient access to both hierarchical semantic information and multiple- resolution spatial data, it is well suited to problems that are best treated at multiple levels of abstraction. The tight integration between semantic and spatial representation allows effective control of both the search space and the solution set size. The posed problem is to determine one or more “good” routes for a wheeled vehicle from the start to the goal state depicted in Figure 18.11. Stage 1 begins by performing a spatially anchored search (i.e., anchored by both the start and goal states) for extended mobility barriers associated with both the cultural and geographic feature database. As shown in Figure 18.12, the highest level-of-abstraction representation FIGURE 18.11 Domain mobility map for path development algorithm. Start Goal Mountain 1 Road 2 Bridge 2 Road 1 Lake 1 Forest 1 River 2 River 1 Marsh 1  Scale 0 100 m ©2001 CRC Press LLC of the object representation of space (i.e., the top-level of the pyramid) indicates that a river, which represents an extended ground-mobility barrier, exists in the vicinity of both the start and the goal states. At this level of abstraction, it cannot be determined whether the extended barrier lies between the two points. The pyramid data structure supports highly focused, top-down searching to determine whether ground travel between the start and goal states is blocked by a river. At the next higher resolution level, however, ambiguity remains. Finally, at the third level of the pyramid, it can be confirmed that a river lies between the start and goal states. Therefore, an efficient, global path strategy can be pursued that requires breaching the identified barrier. Consequently, bridges, suitable fording locations, or bridging operations become candidate subgoals. If, on the other hand, no extended barrier had been discovered in the cells shared by the start and goal states (or in any intervening cells) at the outset of Stage 1 analysis, processing would terminate without generating any intervening subgoals. In this case, Stage 1 analysis would indicate that a direct path to the goal is feasible. While conventional path planning algorithms operate strictly in the spatial domain, a flexible top- down path-planning algorithm supported by an effectively integrated semantic and spatial database can operate across both the semantic and spatial domains. For example, suppose nearby bridges are selected as the primary subgoals. Rather than perform spatial search, direct search of the semantic object (River 1) could determine nearby bridges. Figure 18.13 depicts attributes associated with that semantic object, including the location of a number of bridges that cross the river. To simplify the example, only the closest bridge (Bridge 1) will be selected as a candidate subgoal (denoted SG 1,1 ). Although this bridge could have been located via spatial search in both directions along the river (from the point at which a line from the start to the goal state intersects River 1), a semantic-based search is more efficient. To determine if one or more extended barriers lie between SG 1,1 and the goal state, a spatial search is reinitiated from the subgoal in the direction of the goal state. High-level spatial search within the pyramid data structure reveals another potential river barrier. Top-down spatial search once again verifies the FIGURE 18.12 To p-down multiple resolution spatial search, from the start toward the goal node, reveals the existence of a river barrier. Multiple resolution, true 2- D spatial indexing to high resolution, memory-efficien t representations of point, lines and regions Bridge 1 Bridge 2 Road 2 Marsh 1 Forest 1 Lake 1 Road 1 River 1 Road River River River Road River River River Road River Road Road River Road River Road River Road River minimal region quadtree boundary list Lake 1 object River 2 River 1 segment Start Goal ©2001 CRC Press LLC existence of a second extended barrier (River 2). Just as before, the closest bridging location, denoted as SG 1,2 , is identified by evaluating the bridge locations maintained by the semantic object (River 2). Spatial search from Bridge 2 toward the goal state reveals no extended barriers that would interfere with ground travel between SG 1,2 and the goal state. As depicted in Figure 18.14, the first stage of the path development algorithm generates a single path consisting of the three subgoal pairs (start, SG 1,1 ), (SG 1,1 , SG 1,2 ), (SG 1,2 , goal) satisfying the global objective of reaching the goal state by breaching all extended barriers. Thus, at the conclusion of Stage 1, the primary alternatives to path flow have been identified. In Stage 2, road segments connecting adjacent subgoals that are on or near the road network must be identified. The semantic object representation of the bridges identified as subgoals during the Stage 1 analysis also identify their road association; therefore, a road network solution potentially exists for the subgoal pair (SG 1,1 , SG 1,2 ). Algorithms are widely available for efficiently generating minimum distance paths within a road network. As a result of this analysis, the appropriate segments of Road 1 and Road 2 are identified as members of the candidate solution set (shown in bold lines in Figure 18.14). Next, the paths between the start state and SG 1,1 are investigated. SG 1,1 is known to be on a road and the start state is not; therefore, determining whether the start state is near a road is the next objective. FIGURE 18.13 Semantic object database for the path development algorithm. FIGURE 18.14 Sub-goals associated with all three stages of the path development algorithm. Headwater Tr ibutaries Max depth Bridges Bridge 1 Bridge 17 Location Length Width Capacity Assoc. roads Road 1 Connect city Connect road Road 2 River 1 Bridge 1 Road 1  River 2 Assoc. bridges  Bridge 2 Bridge 2 Road 2 Start Goal Mountain 1 Road 2 Bridge 2 Bridge 1 Road 1 Lake 1 Forest 1 River 2 River 1 Marsh 1  SG 1,2 SG 2,2  SG 1,1 SG 2,1 Scale 0100 m SG 3,1 SG 3,3 SG 3,4 SG 3,2 SG 3,7 SG 3,6 SG 3,5 SG 3,8 SG 3,9 SG 3,10 ©2001 CRC Press LLC Suppose the assessment is based on the fuzzy qualifier near shown in Figure 18.15. Because the detailed spatial relations between features cannot be economically maintained with a semantic representation, spatial search must be used. Based on the top-down, multiple-resolution object representation of space, a road is determined to exist within the vicinity of the start node. A top-down spatially localized search within the pyramid efficiently reveals the closest road segment to the start node. Computing the Euclidean distance from that segment to the start node, the state node is determined to be near a road with degree of membership 0.8. Because the start node has been determined to be near a road, in addition to direct overland travel toward Bridge 1 (start, SG 1,1 ), an alternative route exists based on overland travel to the nearest road (subgoal SG 2,1 ) followed by road travel to Bridge 1 (SG 2,1 , SG 1,1 ). Although a spectrum of variants exists between direct travel to the bridge and direct travel to the closest road segment, at this level of abstraction only the primary alternatives must be identified. Repeating the analysis for the path segment (SG 1,2 , goal), the goal node is determined to be not near any road. Consequently, overland route travel is required for the final leg of the route. In Stage 3, all existing nonroad path segments are refined based on more local evaluation criteria and mobility constraints. First, large barriers, such as lakes, marshes, and forests are considered. Straight-line search from the start node to SG 1,1 reveals the existence of a large lake. Because circumnavigation of the lake is required, two subgoals are generated (SG 3,1 and SG 3,2 ) as shown in Figure 18.14, one representing clockwise travel and the other counter-clockwise travel around the barrier. In a similar manner, spatial search from the start state toward SG 2,1 reveals a large marsh, generating, in turn, two additional subgoals (SG 3,3 and SG 3,4 ). Spatial search from both SG 3,3 toward SG 2,1 reveals a forest obstacle (Forest 1). Assuming that the forest density precludes wheeled vehicle travel, two more subgoals are generated representing a northern route (SG 3,5 ) and a southern route (SG 3,6 ) around the forest. Because a road might pass through the forest, a third strategy must be explored (road travel through the forest). The possibility of a road through the forest can be investigated by testing containment or generating the intersection between Forest 1 and the road database. The integrated spatial/semantic database discussed in Section 18.6 provides direct support to contain- ment testing and intersection operations. With a strictly vector-based representation of roads and regions, intersection generation might require interrogation of a significant portion of the road database; however, the quadtree-indexed vector spatial representation presented permits direct spatial search of that portion of the road database that is within Forest 1. 1 Suppose a dirt road is discovered to intersect the forest. Since no objective criterion exists for evaluating the “best” subpath(s) at this level of analysis, an additional subgoal (SG 3,7 ) is established. To illustrate the benefits of deferring decision making, consider the fact that although the length of the road through the forest could be shorter than the travel distance around the forest, the road may not enter and exit the forest at locations that satisfy the overall path selection criteria. FIGURE 18.15 Membership function for fuzzy metric “near.” Distance 1 0.5 1 km Near 0.8 ©2001 CRC Press LLC Continuing with the last leg of the path, spatial search from SG 1,2 to the goal state identifies a mountain obstacle. Because of the inherent flexibility of a constraint-satisfaction-based problem-solving paradigm, a wide range of local path development strategies can be considered. For example, the path could be constrained to employ one or more of the following strategies: 1. Circumnavigate the obstacle (SG 3,8 ). 2. Remain below a specified elevation (SG 3,9 ). 3. Follow a minimum terrain gradient SG 3,10 . Figure 18.16 shows the path-plan subgoal graph following Stage 1, Stage 2, and Stage 3. Proceeding in a top-down fashion, detailed paths between all sets of subgoals can be recursively refined based on the evaluation of progressively more local evaluation criteria and domain constraints. Path evaluation criteria at this level of abstraction might include (1) the minimum mobility resistance, (2) minimum terrain gradient, or (3) maximal speed paths. Tr aditional path planning algorithms generate global solutions by using highly local nearest-neighbor path extension strategies (e.g., gradient descent), requiring the generation of a combinatorial number of paths. Global optimization is typically achieved by rank ordering all generated paths against an evaluation metric (e.g., shortest distance or maximum speed). Supported by the semantic/spatial database kernel, the top-down path-planning algorithm just outlined requires significantly smaller search spaces when compared to traditional, single-resolution algorithms. Applying a single high-level constraint that elim- inates the interrogation of a single 1 km × 1 km resolution cell, for example, could potentially eliminate search-and-test of as many as 10,000 10 m × 10 m resolution cells. In addition to efficiency gains, due to its reliance on a hierarchy of constraints, a top-down approach potentially supports the generation of more robust solutions. Finally, because it emulates the problem-solving character of humans, the approach lends itself to the development of sophisticated algorithms capable of generating intuitively appealing solutions. FIGURE 18.16 Path development graph following (a) stage 1, (b) stage 2, and (c) stage 3. Stage 1 Stage 2 Stage 3 START Bridge 1 Bridge 2 GOAL  SG 1,1 SG 1, 2 (a) Overland Road Overland (b) SG 2,1 Overland Road SG 3,1 SG 1,1 Lake 1 Overland Overland Overland Overland SG 3,4 SG 3,3 SG 3,8 (c) SG 3,5 SG 3,6 SG 3,7 SG 2,2 SG 3,9 SG 3,10 SG 1,1 SG 2,2 SG 1,2 SG 3,2 SG 1,2 SG 2,1 ©2001 CRC Press LLC In summary, the hierarchical path development algorithm 1. Employs a reasoning approach that effectively emulates manual approaches, 2. Can be highly robust because constraint sets are tailored to a specific vehicle class, 3. Is dynamically sensitive to the current domain context, and 4. Generates efficient global solutions. The example outlined in this section demonstrates the utility of the database kernel presented in Section 18.6. By facilitating the efficient, top-down, spatially anchored search and fully integrated seman- tic and the spatial object search, the spatial/semantic database provides direct support to a wide range of demanding, real-world problems. 18.8 Summary and Conclusions Situation awareness development for remote sensing applications relies on the effective combination of a wide range of data and knowledge sources, including the maximal use of relevant sensor-derived (e.g., imagery, overlays, and video) and nonsensor-derived information (e.g., topographic features; cultural features; and past, present, and future weather conditions). Sensor-supplied information provides dynamic information that feeds the analysis process; however, relatively static domain-context knowledge provides equally valuable information that constrains the interpretation of sensor-derived information. Due to the potentially large volume of both sensor and nonsensor-derived databases, the character and capability of the supporting database management system can significantly impact both the effectiveness and the efficiency of machine-based reasoning. This chapter outlined a number of top-down design considerations for an object database kernel that supports the development of both effective and efficient data fusion algorithms. At the highest level of abstraction, the near-optimal database kernel consists of two classes of objects: semantic and spatial. Because conventional OODBMS provide adequate support to semantic object representations, the chapter focused on the design for the spatial object representation. A spatial object realization consisting of an object representation of 2-D space integrated with a hybrid spatial representation of individual point, line, and region features was shown to achieve an effective compromise across all design criteria. An object representation of 2-D space provides a spatial object hierarchy metaphorically similar to a conventional semantic object hierarchy. Just as a semantic object hierarchy supports top-down semantic reasoning, a spatial object hierarchy supports top-down spatial reasoning. A hybrid spatial representation, the quadtree-indexed vector representation, supports an efficient top-down search and analysis and high-precision refined analysis of individual spatial features. Both the object representation of 2-D space and the multiple-resolution representation of individual spatial features employ the identical quadtree decomposition. Therefore, the quadtree-indexed vector representation is a natural extension of the object representation of 2-D space. Acknowledgment Preparation of this chapter was funded by the U.S. Army CECOM I2WD, Fort Monmouth, NJ. Reference 1. R. Antony, Principles of Data Fusion Automation, Artech House Inc., Boston, 1995. ©2001 CRC Press LLC 19 Removing the HCI Bottleneck: How the Human-Computer Interface (HCI) Affects the Performance of Data Fusion Systems* 19.1 Introduction 19.2 A Multimedia Experiment SBIR Objective • Experimental Design and Test Approach CBT Implementation 19.3 Summary of Results 19.4 Implications for Data Fusion Systems Acknowledgment References 19.1 Introduction During the past two decades, an enormous amount of effort has focused on the development of automated multisensor data systems. 1-3 These systems seek to combine data from multiple sensors to improve the ability to detect, locate, characterize, and identify targets. Since the early 1970s, numerous data fusion systems have been developed for a wide variety of applications, such as automatic target recognition, identification-friend-foe-neutral (IFFN), situation assessment, and threat assessment. 4 At this time, an extensive legacy exists for department of defense (DoD) applications. That legacy includes a hierarchical process model produced by the Joint Directors of Laboratories (shown in Figure 19.1), a taxonomy of algorithms, 5 training material, 6 and engineering guidelines for algorithm selection. 7 The traditional approach for fusion of data progresses from the sensor data (shown on the left side of Figure 19.1) toward the human user (on the right side of Figure 19.1). Conceptually, sensor data are preprocessed using signal processing or image processing algorithms. The sensor data are input to a Level 1 fusion process that involves data association and correlation, state vector estimation, and identity *This chapter is based on a paper by Mary Jane Hall et al., Removing the HCI bottleneck: How the human computer interface (HCI) affects the performance of data fusion systems, Proceedings of the 2000 MSS National Symposium on Sensor and Data Fusion, Vol. II, June 2000, pp. 89–104. Mary Jane M. Hall TECH REACH Inc. Capt. Sonya A. Hall Minot AFB Timothy Tate Naval Training Command ©2001 CRC Press LLC estimation. The Level 1 process results in an evolving database that contains estimates of the position, velocity, attributes, and identities of physically constrained entities (e.g., targets and emitters). Subse- quently, automated reasoning methods are applied in an attempt to perform automated situation assess- ment and threat assessment. These automated reasoning methods are drawn from the discipline of artificial intelligence. Ultimately, the results of this dynamic process are displayed for a human user or analyst (via a human- computer interface (HCI) function). Note that this description of the data fusion process has been greatly simplified for conceptual purposes. Actual data fusion processing is much more complicated and involves an interleaving of the Level 1 through Level 3 (and Level 4) processes. Nevertheless, this basic orientation is often used in developing data fusion systems: the sensors are viewed as the information source and the human is viewed as the information user or sink. In one sense, the rich information from the sensors (e.g., the radio frequency time series and imagery) is compressed for display on a small, two-dimensional computer screen. Bram Ferran, the vice president of research and development at Disney Imagineering Company, recently pointed out to a government agency that this approach is a problem for the intelligence community. Ferran 8 argues that the broadband sensor data are funneled through a very narrow channel (i.e., the computer screen on a typical workstation) to be processed by a broadband human analyst. In his view, the HCI becomes a bottleneck or very narrow filter that prohibits the analyst from using his extensive pattern recognition and analytical capability. Ferran suggests that the computer bottleneck effectively defeats one million years of evolution that have made humans excellent data gatherers and processors. Interestingly, Clifford Stoll 9,10 makes a similar argument about personal computers and the multimedia misnomer. Researchers in the data fusion community have not ignored this problem. Waltz and Llinas 3 noted that the overall effectiveness of a data fusion system (from sensing to decisions) is affected by the efficacy of the HCI. Llinas and his colleagues 11 investigated the effects of human trust in aided adversarial decision support systems, and Hall and Llinas 12 identified the HCI area as a key research need for data fusion. Indeed, in the past decade, numerous efforts have been made to design visual environments, special displays, HCI toolkits, and multimedia concepts to improve the information display and analysis process. Examples can be found in the papers by Neal and Shapiro, 13 Morgan and Nauda, 14 Nelson, 15 Marchak and Whitney, 16 Pagel, 17 Clifton, 18 Hall and Wise, 19 Kerr et al., 20 Brendle, 21 and Steele, Marzen, and Corona. 22 A particularly interesting antisubmarine warfare (ASW) experiment was reported by Wohl et al. 23 Wohl and his colleagues developed some simple tools to assist ASW analysts in interpreting sensor data. The tools were designed to overcome known limitations in human decision making and perception. Although very basic, the support tools provided a significant increase in the effectiveness of the ASW analysis. The experiment suggested that cognitive-based tools might provide the basis for significant improvements in the effectiveness of a data fusion system. FIGURE 19.1 Joint directors of laboratories (JDL) data fusion process model. Sources Human Computer Interaction DATA FUSION DOMAIN Source Pre-Processing Level One Object Refinement Level Two Situation Refinement Level Three Threat Refinement Level Four Process Refinement Database Management System Support Database Fusion Database ©2001 CRC Press LLC In recent years, there have been enormous advances in the technology of human computer interfaces. Advanced HCI devices include environments such as: •A three-dimensional full immersion NCSA CAVE™, illustrated in Figure 19.2, which was developed at the University of Illinois, Champaign-Urbana campus (http://www.ncsa.uiuc.edu/VEG/ncsa- CAVE.html). •Haptic interfaces to allow a person to touch and feel a computer display. 24 •Wearable computers for augmented reality. 25 The technology exists to provide very realistic displays and interaction with a computer. Such realism can even be achieved in field conditions using wearable computers, heads-up displays, and eye-safe laser devices that paint images directly on the retina. Unfortunately, advances in understanding of human information needs and how information is processed have not progressed as rapidly. There is still much to learn about cognitive models and how humans access, reason with, and are affected by information. 26-29 That lack of understanding of cognitive- based information access and the potential for improving the effectiveness of data fusion systems moti- vated the research described in this chapter. 19.2 A Multimedia Experiment 19.2.1 SBIR Objective Under a Phase II SBIR effort (Contract No. N00024-97-C-4172), Tech Reach, Inc. (a small company located in State College, PA) designed and conducted an experiment to determine if a multimode information access approach improves learning efficacy. The basic concept involved the research hypoth- esis that computer-assisted training, which adapts to the information access needs of individual students, significantly improves training effectiveness while reducing training time and costs. The Phase II effort included •Designing, implementing, testing, and evaluating a prototype computer-based training (CBT) system that presents material in three formats (emphasizing aural, visual, and kinesthetic presen- tations of subject material); •Selecting and testing an instrument to assess a student’s most effective learning mode; •Developing an experimental design to test the hypothesis; •Conducting a statistical analysis to affirm or refute the research hypothesis; •Documenting the results. FIGURE 19.2 Example of a full immersion 3-D (HCI). ©2001 CRC Press LLC 19.2.2 Experimental Design and Test Approach The basic testing concept for this project is shown in Figure 19.3 and described in detail by M. J. Hall. 30 The selected sample consisted of approximately 100 Penn State ROTC students, 22 selected adult learners (i.e., post-secondary adult education students), and 120 U.S. Navy (USN) enlisted personnel at the USN Atlantic Fleet Training Center at DAM NECK (Virginia Beach, VA). This sample was selected to be representative of the population of interest to the U.S. Navy sponsor. As shown in Figure 19.3, the testing was conducted using the following steps: 1. Initial Data Collection: Data were collected to characterize the students in the sample (including demographic information, a pretest of the students’ knowledge of the subject matter, and a learning style assessment using standard test instruments). 2. Test Group Assignment: The students were randomly assigned to one of three test groups. The first group used the CBT that provided training in a mode that matched their learning preference mode as determined by the CAPSOL learning styles inventory instrument. 31 The second group trained using the CBT that emphasized their learning preference mode as determined by the student’s self-selection. Finally, the third group was trained using the CBT that emphasized a learning preference mode that was deliberately mismatched with the student’s preferred mode (e.g., utilization of aural emphasis for a student whose learning preference is known to be visual). 3. CBT Training: Each student was trained on the subject matter using the interactive computer- based training module (utilizing one of the three information presentation modes: visual, aural, or kinesthetic). 4. Post-testing: Post-testing was conducted to determine how well the students mastered the training material. Three post-tests were conducted: (a) an immediate post-test after completion of the training material, (b) an identical comprehension test administered one hour after the training session, and (c) an identical comprehensive test administered one week after the initial training session. The test subjects were provided with a written explanation of the object of the experiment and its value to the DoD. Testing was conducted in four locations, as summarized in Table 19.1. Test conditions FIGURE 19.3 Overview of a test concept. Computer-based Training Technical Subject TEST POPULATION Demographic Data Pre-Test of Subject Matter Learning Style Assessment - self assessment (learning preference) INITIAL DATA COLLECTION TEST GROUP ASSIGNMENT A.   Assigned to correct learning style (based on learning style assessment test) B. Assigned to correct learning style (based on personal preference) C. Assigned to wrong learning style (mismatched learning style) , , , CBT TRAINING • immediate post test  • one-hour post test • one-week post test  POST TESTING Learning Comprehension Test Scores STATISTICAL ANALYSIS (in consultation with the Penn State Statistical Center and Psychologist) Project Report and Analysis [...]... Techniques in Multisensor Data Fusion, Artech House Inc., Norwood, MA, 199 2 3 Waltz, E and Llinas, J., Multisensor Data Fusion, Artech House Inc., Norwood, MA, 199 0 4 Hall, D.L., Linn, R.J., and Llinas, J., A survey of data fusion systems, in Proc SPIE Conf on Data Structures and Target Classification, 1 490 , SPIE, 199 1, 13 5 Hall, D.L and Linn, R.J., A taxonomy of algorithms for multisensor data fusion, in... fusion, in Proc 199 0 Joint Service Data Fusion Symp., Johns Hopkins Applied Research Laboratory, Laurel, MD, 199 0, 593 6 Hall, D.L., Lectures in Multisensor Data Fusion, Artech House, Inc., Norwood, MA, 2000 7 Hall, D.L and R.J Linn, Algorithm selection for data fusion systems, in Proc of the 198 7 Tri-Service Data Fusion Symp., Johns Hopkins Applied Physics Laboratory, Laurel, MD, 198 7, 100 8 Ferran,... multiple heterogeneous data source access, in Proc Sixth Joint Service Data Fusion Symposium, 1, Johns Hopkins University, Applied Physics Laboratory, Laurel, MD, 199 3, 565 19 Hall, D.L and Wise, J.H., The use of multimedia technology for multisensor data fusion training, Proc Sixth Joint Service Data Fusion Symp., 1, Johns Hopkins University, Applied Physics Laboratory, Laurel, MD, 199 3, 243 20 Kerr, R... 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HCI for data fusion, we may be able to re-engage the human in the data fusion process and leverage our evolutionary heritage Acknowledgment Funding for this research was provided under a Phase II SBIR by NAVSEA, Contract No N00024 -97 -C4172, September 199 7 ©2001 CRC Press LLC References 1 Hall, D.L and Llinas, J., An introduction to multisensor data fusion, in Proc IEEE, Vol 85, No 1, January 199 7 2 Hall,... report, State University of New York at Buffalo, Dept of Industrial Engineering, February 199 7 12 Hall, D.L and Llinas, J., A challenge for the data fusion community I: research imperatives for improved processing, in Proc 7th Nat Symp on Sensor Fusion, Albuquerque, NM, 199 4 13 Neal, J.G and Shapiro, S.C., Intelligent integrated interface technology, in Proc 198 7 Tri-Service Data Fusion Symp., Johns Hopkins... 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Hall, D.L. and Linn, R.J., A taxonomy of algorithms for multisensor data fusion, in Proc. 199 0 Joint Service Data Fusion Symp. , Johns Hopkins. Digitization of the Battlespace IV , 37 09, Orlando, Florida, 199 9, 205. 23. Wohl, J.G. et al., Human cognitive performance in ASW data fusion, in Proc. 198 7 Tri-Service Data Fusion Symp. . Llinas, J., Multisensor Data Fusion , Artech House Inc., Norwood, MA, 199 0. 4. Hall, D.L., Linn, R.J., and Llinas, J., A survey of data fusion systems, in Proc. SPIE Conf. on Data Structures