Share Email Print

Proceedings Paper

Cluster dynamics and object resolution
Author(s): Albert J. Perrella
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

This paper documents an analytical effort that looks at the expectation of being able to resolve individual members of a cluster of objects as a function of the parameters of time after deployment, the number and distribution of objects in the cluster, their relative separation velocities, sensor resolution capability, and the shape of the cluster -- essentially local object density. Multiple methods of modeling object clusters were investigated and found to be equivalent in their results. A simple set of equations has been derived that fits modeled data over a wide range of the parameter variations. For N objects in a cluster of average density equals d objects per resolution cell; R approximately equals N (DOT) e-d equals the expected number of objects resolved, and P approximately equals (N - R)/d equals the expected number of subclusters perceived. For uniform cluster densities, d is inversely proportional to time squared, and a method is shown for calculating d for non-uniform cluster densities. In addition, an approximately constant relationship between the number of objects perceived and the number of resolved objects is shown; R approximately equals P2/N. Several applications of these relationships which are of interest to the Strategic Defense Initiative (SDI) are examined, including the `Cheshire Cat Effect' wherein the number of perceived objects as a function of resolution and sensor sensitivity is discussed. In addition, system level implications of the effects of target density during boost phase and during the cluster tracking phase of mid-course are covered. The behavior of large numbers of clusters in a threat tube is examined and characterized as the individual clusters overlap each other as they expand and form a `supercluster.' An equilibrium limit of resolution possible within a `supercluster' is shown.

Paper Details

Date Published: 25 August 1992
PDF: 20 pages
Proc. SPIE 1698, Signal and Data Processing of Small Targets 1992, (25 August 1992); doi: 10.1117/12.139377
Show Author Affiliations
Albert J. Perrella, Institute for Defense Analyses (United States)

Published in SPIE Proceedings Vol. 1698:
Signal and Data Processing of Small Targets 1992
Oliver E. Drummond, Editor(s)

© SPIE. Terms of Use
Back to Top