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22 August 2000 New fuzzy set tools to aid in predictive sensor fusion
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Abstract
Sensor and algorithm fusing is playing an increasing role in many application domains. As detection and recognition problems become more complex and costly, it is apparent that no single source of information can provide the ultimate solution. However, complementary information can be derived from multiple sources. Given a set of outputs form constituent source there are many frameworks within which to combine the pieces into a more definitive answer. A more fundamental question, though, is the following. If we know the general characteristics of a set of sensors, can we predict the value added by fusing their outputs together. Correspondingly, can we specify the needed characteristics of a new sensor/algorithm to add to an existing suite to gain a desired improvement in performance. These questions are difficult and, of course, coupled to the fusion framework. In this paper, we consider these questions in the context of fuzzy set theory, taking a step towards an answer. In particular, we look at a quantitative analysis of sensor system fusion of landmine detection locations. We develop new tools to examine the performance of detection position errors, modeled by vectors of fuzzy sets, in a simulation environment. The approach is shown with general data obtained from an advanced technology demonstration.
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James M. Keller, Sansanee Auephanwiriyakul, and Paul D. Gader "New fuzzy set tools to aid in predictive sensor fusion", Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); https://doi.org/10.1117/12.396237
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