handbook of multisensor data fusion phần 8 ppt

©2001 CRC Press LLC •The dynamic range of measurements to be obtained in the system (with the attendant concern for unacceptable quantization errors in the reported data). A standard coordinate system does not imply that each internetted platform will perform all of its tracking or navigational calculations in this reference frame. The frame selected for internal processing is depen- dent on what is being solved. For example, when an object’s trajectory needs to be mapped on the earth, the WGS84 is a natural frame for processing. On the other hand, ballistic objects (e.g., spacecraft, ballistic missiles, and astronomical bodies) are most naturally tracked in an inertial system, such as the FK5 system of epoch J2000.0. Each sensor platform will require a set of well-defined transformation matrices relating the local frame to the network standard one (e.g., for multi-platform sensor data fusion). 17 Misalignment compensation. Multisensor data fusion processing enables powerful alignment tech- niques that involve no special hardware and minimal special software. Systematic alignment errors can be detected by associating reports on entities of opportunity from multiple sensors. Such techniques have been applied to problems of mapping images to one another (or for rectifying one image to a given reference system). Polynomial warping techniques can be implemented without any assumptions con- cerning the image formation geometry. A linear least squares mapping is performed based on known correspondences between a set of points in the two images. Alignment based on entities of opportunity presupposes correct association and should be performed only with high confidence associations. A high confidence in track association of point source tracks is supported by •A high degree of correlation in track state, given a constant offset •Reported attributes (features) that are known a priori to be highly correlated and to have reasonable likelihood of being detected in the current mission context •A lack of comparable high kinematic and feature correlation in conflicting associations among sensor tracks. Confidence normalization (evidence conditioning). In many cases, sensors/sources provide some indication of the confidence to be assigned to their reports or to individual data fields. Confidence values can be stated in terms of likelihoods, probabilities, or ad hoc methods (e.g., figures of merit). In some cases, there is no reporting of confidence values; therefore, the fusion system must often normalize confidence values associated with a data report and its individual data attributes. Such evidential condi- tioning uses models of the data acquisition and measurement process, ideally including factors relating to the entity, background, medium, sensor, reference system, and collection management performance. 16.4.3.2.2 Data Association Data association uses the commensurate information in the data to determine which data should be associated for improved state estimation (i.e., which data belongs together and represents the same physical object or collaborative unit, such as for situation awareness). This section summarizes the top- level data association functions. The following overview summarizes the top-level data association functions. Mathematically, deter- ministic data association is a labeled set-covering decision problem: given a set of prepared input data, the problem is to find the best way to sort the data into subsets where each subset contains the data to be used for estimating the state of a hypothesized entity. This collection of subsets must cover all the input data and each must be labeled as an actual target, false alarm, or false track. The hypothesized groupings of the reports into subsets describe the objects in the surveillance area. Figure 16.21(a) depicts the results of a single scan by each of three sensors, A, B, and C. Reports from each sensor — e.g., reports A 1 and A 2 — are presumed to be related to different targets (or one or both may be false alarms). Figure 16.21(a) indicates two hypothesized coverings of a set, each containing two subsets of reports — one subset for each target hypothesized to exist. Sensor resolution problems are treated by allowing the report subsets to overlap wherever one report may originate from two objects; e.g., the sensor C 1 report ©2001 CRC Press LLC 17 Studies and Analyses within Project Correlation: An In-Depth Assessment of Correlation Problems and Solution Techniques* 17.1 Introduction Background and Perspectives on This Study Effort 17.2 A Description of the Data Correlation (DC) Problem 17.3 Hypothesis Generation Characteristics of Hypothesis Generation Problem Space • Solution Techniques for Hypothesis Generation • HG Problem Space to Solution Space Map 17.4 Hypothesis Evaluation Characterization of the HE Problem Space • Mapping of the HE Problem Space to HE Solution Techniques 17.5 Hypothesis Selection The Assignment Problem • Comparisons of Hypothesis Selection Techniques • Engineering an HS Solution 17.6 Summary References 17.1 Introduction The “correlation” problem is one in which both measurements from multiple sensors and additional inputs from multiple nonsensor sources must be optimally allocated to estimation processes that produce (through data/information fusion techniques) fused parameter estimates associated with hypothetical *This chapter is based on a paper by James Llinas et al., Studies and analyses within project correlation: an in- depth assessment of correlation problems and solution techniques, in Proceedings of the 9th National Symposium on Sensor Fusion, March 12–14, 1996, pp. 171–188. James Llinas State University of New York Capt. Lori McConnell USAF/Space Warfare Center Christopher L. Bowman Consultant David L. Hall The Pennsylvania State University Paul Applegate Consultant ©2001 CRC Press LLC 18 Data Management Support to Tactical Data Fusion 18.1 Introduction 18.2 Database Management Systems 18.3 Spatial, Temporal, and Hierarchical Reasoning 18.4 Database Design Criteria Intuitive Algorithm Development • Efficient Algorithm Performance • Data Representation Accuracy • Database Performance Efficiency • Spatial Data Representation Characteristics • Database Design Tradeoffs 18.5 Object Representation of Space . Low-Resolution Spatial Representation • High-Resolution Spatial Representation • Hybrid Spatial Feature Representation 18.6 Integrated Spatial/Nonspatial Data Representation 18.7 Sample Application Problem-Solving Approach • Detailed Example 18.8 Summary and Conclusions Acknowledgment Reference 18.1 Introduction Historically, data fusion automation directed at tactical situation awareness applications employed data- base management systems (DBMS) primarily for storage and retrieval of sensor-derived parametric and text-based data, fusion products, and algorithm components such as templates and exemplar sets. With the increased emphasis over the last decade on multimedia data sources, such as imagery, video, and graphic overlays, the role of database management systems has expanded dramatically. As a consequence, DBMS are now widely recognized as a critical, and perhaps limiting component of the overall system design. To enhance situation awareness capability, fusion algorithms will increasingly seek to emulate the problem-solving proficiency of human analysts by employing deep problem domain knowledge that is sensitive to problem context. In tactical applications, such contextual knowledge includes existing weather conditions, local natural domain features (e.g., terrain/elevation, surface materials, vegetation, rivers, and drainage regions), and manmade features (e.g., roads, airfields, and mobility barriers). These data sets represent largely a priori information. Thus, the vast majority of sensor-derived and a priori knowledge bases consist of spatially organized information. For large-scale applications, these data sets must be Richard Antony VGS Inc. ©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 . Press LLC 18 Data Management Support to Tactical Data Fusion 18. 1 Introduction 18. 2 Database Management Systems 18. 3 Spatial, Temporal, and Hierarchical Reasoning 18. 4 Database Design. Performance • Data Representation Accuracy • Database Performance Efficiency • Spatial Data Representation Characteristics • Database Design Tradeoffs 18. 5 Object Representation of Space . . 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