Sensor fusion potential exploitation-innovative architectures and illustrative applications

TLDR

Sensor fusion research typically categorizes approaches by information level—data, feature, or decision—alongside objectives, application domains, sensor types, and suite configurations. This work proposes a generalized input‑output descriptor framework that seeks to exhaustively exploit sensor fusion potential by exploring all I/O‑based fusion modes, including self‑improving architectures and decision‑subset environments. The authors design flexible fusion architectures that enable self‑improving multisensor systems and accommodate decision‑subset scenarios, and they illustrate these designs with real‑world examples.

Abstract

Sensor fusion models have been characterized in the literature in a number of distinctly different ways: in terms of information levels at which the fusion is accomplished; the objectives of the fusion process, the application domain; the types of sensors employed, the sensor suite configuration and so on. The characterization most commonly encountered in the rapidly growing sensor fusion literature based on level of detail in the information is that of the now well known triplet: data level, feature level, and decision level. We consider here a generalized input-output (I/O) descriptor pair based characterization of the sensor fusion process that can be looked upon as a natural out growth of the trilevel characterization. The fusion system design philosophy expounded here is that an exhaustive exploitation of the sensor fusion potential should explore fusion under all of the different I/O-based fusion modes conceivable under such a characterization. Fusion system architectures designed to permit such exploitation offer the requisite flexibility for developing the most effective fusion system designs for a given application. A second facet of this exploitation is aimed at exploring the new concept of self-improving multisensor fusion system architectures wherein the central (fusion system) and focal (individual sensor subsystems) decision makers mutually enhance the other's performance by providing reinforced learning. A third facet is that of investigating fusion system architectures for environments wherein the different local decision makers may only be capable of narrower decisions that span only a subset of decision choices. The paper discusses these flexible fusion system architectures along with related issues and illustrates them with examples of their application to real-world scenarios.