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When single cycle frequency is employed, the existing spectral correlation-signal subspace fitting (SC-SSF) algorithms usually contain two disadvantages: those single-cycle estimators cannot reach the best performance; it is inconvenient to be applied in practice since the right cycle frequency has to be selected. Based on the Jacobi-Anger expansion and the idea of focusing transform, a new approach exploiting multi-cycle frequencies of cyclostationary signal is discussed in this paper. Simulation results demonstrate the effectiveness of the new method.
When single cycle frequency is employed, the existing spectral correlation-signal subspace fitting (SC-SSF) algorithms usually contain the right cycle frequency (those single-cycle estimators can not reach the best performance; it is inconvenient to be applied in practice since the right cycle frequency Based on the Jacobi-Anger expansion and the idea of focusing transform, a new approach exploiting multi-cycle frequencies of cyclostationary signals is discussed in this paper. Simulation results demonstrate the effectiveness of the new method.