Coordination, adaptation, and complexity in decision fusion

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 \u3e 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

Decision Fusion in Non-stationary Environments

A parallel distributed detection system consists of multiple local sensors/detectors that observe a phenomenon and process the gathered observations using inbuilt processing capabilities. The end product of the local processing is transmitted from each sensor/detector to a centrally located data fusion center for integration and decision making. The data fusion center uses a specific optimization criterion to obtain global decisions about the environment seen by the sensors/detectors. In this study, the overall objective is to make a globally-optimal binary (target/non-target) decision with respect to a Bayesian cost, or to satisfy the Neyman-Pearson criterion. We also note that in some cases a globally-optimal Bayesian decision is either undesirable or impractical, in which case other criteria or localized decisions are used. In this thesis, we investigate development of several fusion algorithms under different constraints including sequential availability of data and dearth of statistical information. The main contribution of this study are: (1) an algorithm that provides a globally optimal solution for local detector design that satisfies a Neyman-Pearson criterion for systems with identical local sensors; (2) an adaptive fusion algorithm that fuses local decisions without a prior knowledge of the local sensor performance; and (3) a fusion rule that applies a genetic In addition, we develop a parallel decision fusion system where each local sensor is a sequential decision maker that implements the modified Wald's sequential probability test (SPRT) as proposed by Lee and Thomas (1984).Ph.D., Electrical Engineering -- Drexel University, 201