Empirical analysis of generalised uncertainty measures with dempster shafer fusion

Data Fusion within the evidential reasoning framework is a well established, robust and conservative technique to fuse uncertain information from multiple sensors. A number of fusion methods within this formalism were introduced including Dempster-Shafer Theory (DST) Fusion, Dezert Samarandche Fusion (DSmT), and Smets' Transferable Belief Model (TBM) based fusion. However, the impact of fusion on the level of uncertainty within these techniques has not been studied in detail. While the use of Shannon entropy within the Bayesian fusion is well understood, the measures of uncertainty within the Dempster-Shafer formalism is not widely regarded. In this paper, an uncertainty based technique is proposed to quantify the evolution of DST fusion. This technique is then utilised to determine the optimal combination of sensor information to achieve the least uncertainty in the context of an aircraft identification problem using Electronics Support Measure (ESM) sensors.