In this paper, we propose a Knowledge-based Ubiquitous and Persistent Sensor network (KUPS) for threat assessment, in which “sensor ” is a broad characterization. It refers to 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: situation awareness with fuzzy logic systems and threat parameter estimation with radar sensor networks. Our fuzzy logic systems combine the linguistic knowledge from different intelligent sensors, and our proposed maximum-likelihood (ML) estimation algorithm performs target radar cross section (RCS) parameter estimation. We also show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer-Rao lower bound if the radar pulses follow the Swerling 2 model. Simulations further validate our theoretical results
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.