Direction-finding of cyclostationary sources with minimum-redundancy linear arrays by fourth-order cyclic cumulants

Most communication signals exhibit cyclostationarity and the overall number of signals impinging on the array is often greater than the number of sensors. In this paper, considering these, we propose a direction-finding algorithm to detect non-Gaussian cyclostationary sources by designing minimum-redundancy linear arrays (MRLAs) based on fourth-order cyclic cumulants using ESPRIT. Simulation results show that our method can effectively suppress additive stationary noise and Gaussian noise in environments where the spatial characteristics of noise are unknown, even when the noise shares the same cycle frequency as the signals of interests (SOIs). Moreover, compared with the designed fourth-order cyclic cumulants method using uniform linear array (ULA), the MRLA method is proved to provide better performance in terms of ability to detect and resolve a greater number of sources. More than 2M-2 sources can be estimated with M sensors.