A group of multiple heterogeneous sensors is used to observe events of interest and their readings are aggregated into observation vectors that are used to draw inferences. In this generic environment we wish to integrate data provided by "hard" sensors such as readings of radar and thermal sensors with data provided by "soft" sensors such as reports from humans or context analysis by domain experts. Here we form a probabilistic representation of soft sensor data using Dempster Shafer's belief mass assignment and a consensus operator for combining human opinions with uncertainties. We then use a probability fusion rule proposed by Krzystofowicz and Long to generate a hard and soft data fusion system. This approach brings all sensor outputs to the same probabilistic framework prior to fusion. The formulation is demonstrated through three exercises involving hypothetical and real scenarios.