A stochastic process algebraic abstraction of detection evidence fusion in tactical sensor networks

Abstract

The output of a sensor network intended to detect events or objects generally comprises evidentiary reports of features in the environment that may correspond to those phenomena. Signals from multiple sensors are commonly fused to maximize fidelity of detection through for example synergy between different modes of detection, or simple confirmation. We have previously demonstrated the ability to calculate the meaning of a location report as a probability distribution over potential ground truths by using a stochastic process algebraic model compiled to a discrete-state, continuous-time Markov chain, and performing a transient analysis which resembles the process of parameterizing a Bayesian network. We introduce an approach to representing temporal fusion of multiple heterogeneous sensor detections with different modalities and timing characteristics using a stochastic process algebra. This facilitates analysis of probabilistic properties of the system, and inclusion of those properties into larger models. The formal models are translated into continuous time Markov chains, which provide an important trade-off between the approximation of timing information against complexity of analysis. This is vital to the investigation of analytic computation in real world problems. We illustrate this with an example detection-oriented sensing service model emphasizing the impact of timing. Detection probability and confidence is an essential aspect of the quality of information delivered by a sensing service. The present work is part of an effort to develop a formal event detection calculus that captures the essence of sensor information relating to events, such that features and dependencies can be exploited in re-usable, extendible compositional models. © 2009 SPIE.