Mixed Signal Detection and Symbol Rate Estimation Based on Spectral Coherent Features

Signal detection and RF parameter estimation have received great interest in recent years due to the need for spectrum sensing in rapidly growing cognitive radio and cyber security research. In most conventional signal detection and RF parameter estimation 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 or a spectrally contested environment which often occurs in cyber security applications, multiple signals are often mixed together with significant overlap in spectrum. In our previous work, we have demonstrated the feasibility of using a second order spectrum correlation function (SCF) cyclostationary feature to perform mixed signal detection, but the detection was confined to BPSK modulation. In this paper, we extend our work to QPSK modulation by using a robust algorithm to detect mixed signals and estimate their symbol rate via spectral coherence function (SOF) features. We also evaluate the detection and estimation performance of the proposed algorithm in various channel conditions and signal mixture scenarios. Simulation results confirm the effectiveness of the proposed scheme.