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Proceedings Paper

High-accuracy, low-ambiguity emitter classification using an advanced Dempster-Shafer algorithm
Author(s): Dorwin C. Black; John C. Sciortino; John R. Altoft
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Paper Abstract

High-accuracy, low-ambiguity emitter classification based on ESM signals is critical to the safety and effectiveness of military platforms. Many previous ESM classification techniques involved comparison of either the average observed value or the observed limits of ESM parameters with the expected limits contained in an emitter library. Signal parameters considered typically include radio frequency (RF), pulse repetition interval (PRI), and pulse width (PW). These simple library comparison techniques generally yield ambiguous results because of the high density of emitters in key regions of the parameter space (X-band). This problem is likely to be exacerbated as military platforms are more frequently called upon to conduct operations in littoral waters, where high densities of airborne, sea borne, and land based emitters greatly increase signal clutter. A key deficiency of the simple techniques is that by focusing only on parameter averages or limits, they fail to take advantage of much information contained in the observed signals. In this paper we describe a Dempster-Shafer technique that exploits a set of hierarchical parameter trees to provide a detailed description of signal behavior. This technique provides a significant reduction in ambiguity particularly for agile emitters whose signals provide much information for the algorithm to utilize.

Paper Details

Date Published: 9 August 2004
PDF: 11 pages
Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.542593
Show Author Affiliations
Dorwin C. Black, Naval Research Lab. (United States)
John C. Sciortino, Naval Research Lab. (United States)
John R. Altoft, Altek Systems Inc. (Canada)

Published in SPIE Proceedings Vol. 5429:
Signal Processing, Sensor Fusion, and Target Recognition XIII
Ivan Kadar, Editor(s)

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