Extensions to Adaptive Boolean Decision Fusion

The concepts of distributed decision fusion have shown considerable interest for many years, gaining its start with Tenney and Sandell [1] in the early Eighties. Since then Bayesian detection fusion has shown a great deal of progress, adding considerable depth and flexibility to the decision process [2-4]. It has also added a comparative degree of complexity to the evaluation process. This paper will address these complexities in terms of fusion performance. In addition, we will show that the CFAR process can be an effective means of fusion rule selection. The conditions under evaluation involve the use of three image change detection algorithms (two using SAR images, and one using Electro-Optical Imagery). Each change detection algorithm provides a unique observation of the environment. The Adaptive Boolean Decision Fusion (ABDF) process provides a basis for fusing and interpreting these change events.