Mixed Signal Detection and Parameter Estimation Based on Second-Order Cyclostationary Features
Li Dong · CORE Scholar (Wright State University) · 2015
Mixed Signal Detection and Parameter Estimation based on Second-Order Cyclostationary Features.Signal detection and radio frequency (RF) parameter estimation have received a lot of attention in recent years due to the need of spectrum sensing in many military and civilian communication applications.In most of existing work, the target signal is assumed to be a single RF signal with no overlapping with other RF signals.However, in a spectrally congested and spectrally contested environment, multiple signals are often mixed together at the signal detector with significant overlap in spectrum.Conventional frequency analysis through Fourier transform is not capable of detecting mixed signals with significant spectral overlap.In this thesis, we first demonstrate the feasibility of using second-order cyclostationary feature to perform mixed signal detection.We then use the cyclostationary features to estimate the carrier frequencies of these mixed signals.Next, we extend our work to higher order modulation.We develop a robust algorithm to detect mixed signals and estimate their symbol rates as well as carrier frequencies via spectral coherence function (SOF) features.Furthermore, we evaluate the detection and estimation performances of the proposed algorithm in various channel conditions and signal mixture scenarios.Simulation results confirm the effectiveness of the proposed schemes.iii