Abstract

12.1 Maturity of Data Fusion

Methods and standards for implementation of fusion systems and interfaces are evolving. Discussions and research concerning the nature of and procedures to enhance human-computer interfaces are becoming more prevalent. Architecture selection, implementation, and test processes are still ad hoc, often driven by outdated communication and data-processing limitations, and often dictated by personal taste and corporate and agency culture.

Advances in processor technology and sensor netting techniques have removed many of the limitations of the past. Improved signal-processing techniques and digital sensor technology have reduced the clutter and false-alarm problem. Improved workstations and user interfaces (menus) have broadened the applications of data fusion and interaction of the user with the process.

However, operational limitations of commercial, off-the-shelf hardware and software may inhibit the full use of new data-processing technologies. Commercial operating systems and database management systems (DBMSs) are ill-suited to military and air traffic control (ATC) real-time requirements for sensor data processing. Military and ATC systems must be designed for the worst case as delays at critical times are unacceptable.

In state estimation, data correlation is the largest user of data processing resources, often more than 60 to 70 percent of the total. The key data fusion technology of the 1990s was the multiple-hypothesis tracking concept, developed to handle ambiguous association situations. It theoretically maintains all possible track alternatives. The open-ended number and complexity of the alternatives are almost guaranteed to exceed current CPU capabilities and DBMS limitations.

Online access to SPIE eBooks is limited to subscribing institutions.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data fusion

Sensors

Target detection

Detection and tracking algorithms

Fuzzy logic

Filtering (signal processing)

Artificial neural networks

Back to Top