Mixed Signal Detection and Carrier Frequency Estimation Based on Spectral Coherent Features

Signal detection and RF parameter estimation have received strong interest in recent years due to the need of spectrum sensing in rapidly growing cognitive radio network research. In most of existing work, the target signal is often assumed to be a single primary user signal without overlap in spectrum with other signals. However, in a spectrally congested environment such as cognitive radio network, or in a spectrally contested environment such as a battlefield, multiple signals are often mixed together with significant overlap in spectrum. In our previous work, we have demonstrated the feasibility of using second order spectrum correlation function (SCF) cyclostationary feature to perform mixed signal detection. In this paper, we extend our work to employ a robust algorithm to detect mixed signals and estimate their carrier frequencies via spectral coherence function (SOF) features. We also 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 scheme.