James Llinas

Survey of multisensor data fusion systems

Multisensor data fusion integrates data from multiple sensors (and types of sensors) to perform inferences which are more accurate and specific than those from processing single-sensor data. Levels of inference range from target detection and identification to higher level situation assessment and threat assessment. This paper provides a survey of more than 50 data fusion systems and summarizes their application, development environment, system status and key techniques. The techniques are mapped to a taxonomy previously developed by Hall and Linn (1990); these include positional fusion techniques, such as association and estimation, and identity fusion methods, including statistical methods, nonparametric methods, and cognitive techniques (e.g. templating, knowledge-based systems, and fuzzy reasoning). An assessment of the state of fusion system development is provided.

Assessing the performance of multisensor fusion systems

In recent years, numerous prototypical systems have been developed for multisensor data fusion. A typical data fusion process operates on sensor parametric data (e.g., data related to target position or attribute data) in order to develop an order of battle, provide an evaluation of tactical situations, or assess tactical threats. This model, developed by the Data Fusion Sub- panel (DFS) of the Joint Directors of Laboratories, partitions fusion processing into four conceptual levels. Ancillary functions in a fusion system include the human computer interface, data base management, source-preprocessing functions, and communications. Military applications for data fusion span a broad range including fusion of data on board a single platform for identifying other platforms (e.g., identification--friend or foe--neutral systems), threat warning systems, situation assessment, and threat assessment systems. Large scale systems such as the All-Source Analysis System (ASAS) or the Joint Surveillance, Targeting, and Reconnaissance System (JSTARS) provide for direction, coordination, and fusion of both ground-based and airborne sensors to aid in the effective management of a ground based battlefield environment. Such systems have become ever more sophisticated. Indeed, many of the prototypical systems utilize advanced identification techniques such as knowledge-based or expert systems. Dempster-Shafer interface techniques, adaptive neural networks, and sophisticated tracking algorithms. While much research is being performed to develop and apply new algorithms and techniques, little work has been performed to determine how well such methods work or to compare alternative methods against a common problem. The issues of system performance and system effectiveness are keys to establishing how well an algorithm, technique, or collection of techniques perform, and then the extent to which these techniques may be used to achieve success on an operational mission.

Designing a Performance Evaluation Methodology for Data Fusion-capable Tactical Aircraft V

[Abstract] In previous papers, we documented our evolving research that expanded on and formalized an approach to the design of a Performance Evaluation (PE) methodology for Data Fusion (DF)-based tactical aircraft systems. We have shown that the design of a PE process for any multi-sensor or multi-aircraft fusion-based system involves the design of a separate data fusion process involving association and estimation functions for PE purposes per se. Our publications to date have developed the theoretical and architectural groundings for this new PE process, and several case studies have been carried out to show sample implementations of the principles of this new methodology. In addition, some limitedobjective parametric experiments have also been carried out that show the application of the new evaluation methodology for typical tactical aircraft problems. In the current paper, we summarize and cumulate the findings of these past works, and show our most recent AFOSR/AFFTC-sponsored research efforts related to extending the design and application of this methodology to air-to-air engagement problems involving what are called higherlevels of data fusion capability (situation and threat estimation) and the employment of electronic warfare systems. The paper discusses the detailed strategies for data association, metrics estimation, and also the analytical techniques that exploit the formality of the methods of Statistical Design of Experiments (DOE) and Analysis of Variance (ANOVA) for these fusion applications.

Predetection fusion for enhanced surveillance

The objective of this paper is to discuss the issues that are involved in the design of a multisensor data fusion system for surveillance. The system in mind consists primarily of three multifrequency radar sensors. However, the fusion design must be flexible to accommodate additional dissimilar sensors such as IR, EO, ESM, and Ladar. The motivation for the system design is the proof of the fusion concept for enhancing the detectability of small targets in clutter. In the context of down-selecting the proper configuration for multisensor data fusion, the issues of data modeling, fusion approaches, and fusion architectures need to be addressed for the particular application being considered.

Hybrid intelligent control concepts for optimal data fusion

In the post-Cold War era, Naval surface ship operations will be largely conducted in littoral waters to support regional military missions of all types, including humanitarian and evacuation activities, and amphibious mission execution. Under these conditions, surface ships will be much more isolated and vulnerable to a variety of threats, including maneuvering antiship missiles. To deal with these threats, the optimal employment of multiple shipborne sensors for maximum vigilance is paramount. This paper characterizes the sensor management problem as one of intelligent control, identifies some of the key issues in controller design, and presents one approach to controller design which is soon to be implemented and evaluated. It is argued that the complexity and hierarchical nature of problem formulation demands a hybrid combination of knowledge-based methods and scheduling techniques from hard real-time systems theory for its solution.