Title

A Scalable, Portable, Object-Oriented Framework For Parallel Multi-Sensor Data-Fusion Applications In Hpc Systems

Keywords

Data fusion; Hpc systems; Meta-modeling; Object-oriented simulation; Sensor fusion

Abstract

Multi-sensor Data Fusion is synergistic integration of multiple data sets. Data fusion includes processes for aligning, associating and combining data and information in estimating and predicting the state of objects, their relationships, and characterizing situations and their significance. The combination of complex data sets and the need for real-time data storage and retrieval compounds the data fusion problem. The systematic development and use of data fusion techniques are particularly critical in applications requiring massive, diverse, ambiguous, and time-critical data. Such conditions are characteristic of new emerging requirements; e.g., network-centric and information-centric warfare, low intensity conflicts such as special operations, counter narcotics, antiterrorism, information operations and CALOW (Conventional Arms, Limited Objectives Warfare), economic and political intelligence. In this paper, Aximetric presents a novel, scalable, object-oriented, metamodel framework for parallel, cluster-based data-fusion engine on High Performance Computing (HPC) Systems. The data-clustering algorithms provide a fast, scalable technique to sift through massive, complex data sets coming through multiple streams in real-time. The load-balancing algorithm provides the capability to evenly distribute the workload among processors on-the-fly and achieve real-time scalability. The proposed data-fusion engine exploits unique data-structures for fast storage, retrieval and interactive visualization of the multiple data streams.

Publication Date

8-18-2004

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

5434

Number of Pages

295-306

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.542319

Socpus ID

3843152714 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/3843152714

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