Detection And Opportunistic Spectrum Access In Sensor Networks

This thesis examines target detection problems in Radar Sensor Networks (RSN) and opportunistic spectrum access problem in Cognitive Sensor Networks (CSN). First, studies on the Space-Time Adaptive Processing (STAP) and radar waveform design are provided. Investigation into the target detection performance gain of RSN when STAP and radar waveform design are combined in RSN is then performed. Studies in this thesis show that detection performance of RSN using our proposal is superior to that of a single radar system using STAP only. To further studies on target detection, the multi-target detection problem in RSN is also examined. Signal, interference, and noise at radar sensors are modeled and analyzed. At the clusterhead of RSN, a Maximum Likelihood Multi-Target Detection algorithm is proposed to estimate the possible number of targets in a surveillance area. Achieved results show that detection performance of RSN is much better than that of a single radar system in terms of the miss-detection probability and the root mean square error. Besides detection in RSN, this thesis studies an opportunistic spectrum access problem and proposes a spectrum access scheme in CSN. The spectrum access scheme is built using Fuzzy Logic System (FLS); and spectrum access decision is based on: (1) spectrum utilization efficiency of the secondary user (SU); (2) its degree of mobility; and (3) its average distance to primary users (PU). The output of the FLS provides the probabilities of accessing spectrum band for SUs and the SU with the highest probability will be assigned the available spectrum. Studies also show that our scheme performs much better than random access approach.