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Proceedings Paper

An objective multi-sensor fusion metric for target detection
Author(s): S. R. Sweetnich; S. P. Fernandes; J. D. Clark; W. A. Sakla
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Paper Abstract

Target detection is limited based on a specific sensors capability; however, the combination of multiple sensors will improve the confidence of target detection. Confidence of detection, tracking and identifying a target in a multi-sensor environment depends on intrinsic and extrinsic sensor qualities, e.g. target geo-location registration, and environmental conditions 1. Determination of the optimal sensors and classification algorithms, required to assist in specific target detection, has largely been accomplished with empirical experimentation. Formulation of a multi-sensor effectiveness metric (MuSEM) for sensor combinations is presented in this paper. Leveraging one or a combination of sensors should provide a higher confidence of target classification. This metric incorporates the Dempster-Shafer Theory for decision analysis. MuSEM is defined for weakly labeled multimodal data and is modeled and trained with empirical fused sensor detections; this metric is compared to Boolean algebra algorithms from decision fusion research. Multiple sensor specific classifiers are compared and fused to characterize sensor detection models and the likelihood functions of the models. For area under the curve (AUC), MuSEM attained values as high as .97 with an average difference of 5.33% between Boolean fusion rules. Data was collected from the Air Force Research Lab’s Minor Area Motion Imagery (MAMI) project. This metric is efficient and effective, providing a confidence of target classification based on sensor combinations.

Paper Details

Date Published: 20 June 2014
PDF: 12 pages
Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90910M (20 June 2014); doi: 10.1117/12.2049936
Show Author Affiliations
S. R. Sweetnich, Air Force Institute of Technology (United States)
S. P. Fernandes, Central State Univ. (United States)
J. D. Clark, Air Force Institute of Technology (United States)
W. A. Sakla, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 9091:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII
Ivan Kadar, Editor(s)

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