Sequential testing over multiple stages and performance analysis of data fusion

TL;DR

This paper introduces a Bayesian network-based methodology for multi-stage sequential testing in sensor data fusion, enabling performance analysis and efficient decision-making in complex sensor networks.

Contribution

It extends classical probabilistic data fusion with a multi-stage sequential test framework, allowing for staged evidence accumulation and sensor activation control.

Findings

Efficient modeling of multi-stage sensor data fusion using Bayesian networks.

Extension of Wald's sequential test for staged decision processes.

Illustrated approach with example performance analysis.

Abstract

We describe a methodology for modeling the performance of decision-level data fusion between different sensor configurations, implemented as part of the JIEDDO Analytic Decision Engine (JADE). We first discuss a Bayesian network formulation of classical probabilistic data fusion, which allows elementary fusion structures to be stacked and analyzed efficiently. We then present an extension of the Wald sequential test for combining the outputs of the Bayesian network over time. We discuss an algorithm to compute its performance statistics and illustrate the approach on some examples. This variant of the sequential test involves multiple, distinct stages, where the evidence accumulated from each stage is carried over into the next one, and is motivated by a need to keep certain sensors in the network inactive unless triggered by other sensors.