Making Sense of It All: Measurement Cluster Sequencing for Enhanced Situational Awareness with Ubiquitous Sensing

Abstract

Situational awareness methods aim to identify and map what is happening in an operational environment in terms of operational terms that define certain decision-making contexts. The underlying assumption here is that an appropriate decision-making context is either known or can be identified a priori for accurately mapping incoming evidence. However, in many complex and unstructured operational environments where situational awareness systems are most useful (e.g., asymmetric battlegrounds, urban reconnaissance), the decision-making context is neither known a priori nor it is easy to determine by, say subject matter experts. This paper presents a data-driven approach for gaining insights on the decision-making context via judicious processing of ubiquitous soft (i.e., human-based) and hard (e.g., physics-based) data streams generated by voluntarily participating mobile sensors that are traversing the operational environment. In particular, by using spectral clustering in tandem with variable length sequence decoding methods, ubiquitous data stream are clustered and then processed for early identification of specific scenarios of interest (that may have generated the sensor measurements). This will enable a decision-maker to understand emerging situations in the operational environment to set the correct decision-making context and proactively identify what information will be most relevant to reducing uncertainty associated with them. Our approach is illustrated via a simulated example that provides insights into its behavior, performance and sensitivity to parameters.