KUPS: Knowledge-based Ubiquitous and Persistent Sensor networks for Threat Assessment

In this paper, we propose a knowledge-based ubiquitous and persistent sensor networks (KUPS) for threat assessment, of which "sensor" is a broad characterization concept. It means diverse data or information from ubiquitous and persistent sensor sources such as organic sensors and human intelligence sensors. Our KUPS for threat assessment consists of two major steps: threat detection using fuzzy logic systems and threat parameter estimation using radar sensor networks. Our fuzzy logic systems can combine the linguistic knowledge from different intelligent sensors. We propose a maximum-likelihood (ML) estimation algorithm for target RCS parameter estimation, and we show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer-Rao lower bound. Simulations further validate these theoretical results