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Abstract:
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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 . |