Document Type


Date of Award


Degree Name

Doctor of Philosophy in Electrical Engineering - (Ph.D.)


Electrical and Computer Engineering

First Advisor

Moshe Kam

Second Advisor

MengChu Zhou

Third Advisor

Nirwan Ansari

Fourth Advisor

Edwin Hou

Fifth Advisor

Xiaobo Li


A parallel decentralized binary decision fusion architecture employs a bank of local detectors (LDs) that access a commonly-observed phenomenon. The system makes a binary decision about the phenomenon, accepting one of two hypotheses (H0 (“absent”) or H1 (“present”)). The k 1 LD uses a local decision rule to compress its local observations yk into a binary local decision uk; uk = 0 if the k 1 LD accepts H0 and uk = 1 if it accepts H1. The k 1 LD sends its decision uk over a noiseless dedicated channel to a Data Fusion Center (DFC). The DFC combines the local decisions it receives from n LDs (u1, u2, ... , un) into a single binary global decision u0 (u0 = 0 for accepting H0 or u0 = 1 for accepting H1). If each LD uses a single deterministic local decision rule (calculating uk from the local observation yk) and the DFC uses a single deterministic global decision rule (calculating u0 from the n local decisions), the team receiver operating characteristic (ROC) curve is in general non-concave. The system’s performance under a Neyman-Pearson criterion may therefore be suboptimal in the sense that a mixed strategy may yield a higher probability of detection when the probability of false alarm is constrained not to exceed a certain value, a > 0. Specifically, a “dependent randomization” detection scheme can be applied in certain circumstances to improve the system’s performance by making the ROC curve concave. This scheme requires a coordinated and synchronized action between the DFC and the LDs. This study specifies when dependent randomization is needed, and discusses the proper response of the detection system if synchronization between the LDs and the DFC is temporarily lost. In addition, the complexity of selected parallel decentralized binary decision fusion algorithms is studied and the state of the art in adaptive decision fusion is assessed.



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