New Fuzzy Set Fools to Aid in Predictive Sensor Fusion

摘要

Sensor and algorithm fusion is playing an increasing role in many application domains. As detection and recognition problems become more complex and costly (like landmine detection), 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 from constituent sources, there are many frameworks within which to combine the prieces into a more definitive anseer. A more fundametal quesion, 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 totether? Correspondingly, can we s pecify the neededcharacteristics of a nes ensor/algorithm to an existing suite to gain a desired improvement in performance? These questions are difficult and, or 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 anlaysis of sensor system fusion of landmnine 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.