Share Email Print

Proceedings Paper

Multiple-hypothesis-based multiple-sensor spatial data fusion algorithm
Author(s): Dominic S. P. Leung; D. Scot Williams
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

With the projected increase in sophistication of potential threats and their associated countermeasures, the next generation of military operations cannot rely on a single sensor for threat information. The prudent choice of sensor suite in a given setting will avail a wider variety of statistically independent measurements, thus providing more resources for the tracking and classification of targets of interest. One basic problem in data fusion/target classification is to find reliable ways to correlate tracks originating from the same target using the attributes of individual sensor tracks. That practically all sensors provide some form of spatial track data accounts for the fact that spatial data fusion is the most commonly used data fusion technique. An algorithm for correlating all tracks from different sensors based on their spatial characteristics is presented in this paper. The technique used here is an extension of the multiple-hypothesis technique for tracking multiple targets in a cluttered environment. In this multiple-hypothesis correlation approach, all feasible correlation hypotheses are considered and maintained for at least a short period of time. The likelihoods for these hypotheses to be correct are evaluated and updated with the arrival of new data. The unlikely ones are periodically pruned with the most highly probable ones being retained. By using Kalman filtering techniques, the state estimates of each of the fusion hypotheses that survive have a smaller error covariance than any of the tracks from which they were derived.

Paper Details

Date Published: 1 August 1991
PDF: 12 pages
Proc. SPIE 1471, Automatic Object Recognition, (1 August 1991); doi: 10.1117/12.44889
Show Author Affiliations
Dominic S. P. Leung, Martin Marietta Aero & Naval Systems (United States)
D. Scot Williams, Martin Marietta Aero & Naval Systems (United States)

Published in SPIE Proceedings Vol. 1471:
Automatic Object Recognition
Firooz A. Sadjadi, Editor(s)

© SPIE. Terms of Use
Back to Top