In modern systems, there are often many sensors which contribute to the identification of targets at various levels of identity amplification. Some sensors provide type or mode level identification while others provide unique fingerprints of the target of interest. This paper investigates combining of IDs from heterogeneous sensors in a probabilistic fashion to produce a fused multi-level identification. The identification of targets is especially difficult when sensors do not provide confidence metrics. When multiple sensors report differing identifications for the same target, the fusing of the results into a stable set of IDs is complicated. Often sensor integration systems are forced to toggle between candidate IDs that may not capture the breadth of the underlying sensor provided data. This paper describes a methodology for calculating a probabilistic ID based on the evaluation of provided identification data which provides intuitive results when faced with conflicting data. Conditions for choosing which calculation method to use are discussed based on the characteristics of each method.