Data fusion is a process of combining evidence from different information sources in order to make a better judgement. However, multiple sources can provide complementary information that can be used to increase the performance in detection and recognition. There are many frameworks within which to combine these pieces into a more meaningful answer. However, new information added might be redundant or even conflicting with the existing information. These questions arise: can we predict the value added by fusing their outputs together, if we know the general characteristics of a set of sensors. Can we specify the needed characteristics of a new sensor/algorithm to add to an existing suite to gain a desired improvement performance. The characteristic of a new sensor can be in any forms, e.g., the ratio of a target's signal to the clutter's signal, the position resolution etc. In this paper, we consider these questions in the context of fuzzy set theory and in particular, a soft decision level fusion scheme we developed for land mine detection scenarios. Here, we primarily consider the ratio of a target's signal. We develop a tool to estimate a final d-metric when the information form several sensor is fused through the linguistic Choquet fuzzy integral. We utilize this tool in the examination of the performance of d-metrics in a simulation environment. The approach is demonstrated for data obtained from an Advanced Technology Demonstration in vehicle-based mine detection.