Cognitive Radio Network as Wireless Sensor Network (III): Passive target intrusion detection and experimental demonstration

A Cognitive Radio Network (CRN) based Wireless Sensor Network (WSN), as an extension of CRN, is explored for radio frequency (RF) passive target intrusion detection. Compared to a cheap WSN, the CRN based WSN is expected to deliver better results due to its strong communication functions and powerful computing ability. Issues addressed in this paper include experimental architecture, waveform design, and machine learning algorithm for classification. In particular, passive target intrusion is experimentally demonstrated using multiple WARP platforms that serve as the cognitive/sensor nodes. In contrast to traditional localization methods relying on radio propagation properties, the technique used in this research is based on machine learning with measured data, considering complicated multipath environment and high dimensional sensing data collected by the CRN based WSN. Preliminary experimental results are quite encouraging, suggesting that a large-scale CRN based WSN supported by machine learning techniques has promising potential for passive target intrusion detection in harsh RF environments.