As it is known, existing mixed near‐field and far‐field non‐circular source localisation rely on the uniform linear array (ULA). Compared with the ULA, the sparse array can obtain more consecutive lags and a larger physical array aperture. So a novel symmetric flipped nested array (SFNA) is proposed for mixed near‐field and far‐field non‐circular source localisation. At the same time, according to the special geometry of the array, the non‐circular phase information can be removed and the existing algorithm can be used to solve the problem of sparse array estimating the mixed near‐field and far‐field non‐circular signals. First, by exchanging the positions of the subarrays in the traditional nested array and then flipping them, a new SFNA is formed; second, the authors present the maximum consecutive lags and composition method of SFNA. For example, 4 MN  +4 M  −3 consecutive lags can be obtained by using a 2 M  + 2 N  −1 array element; third, in the estimation of mixed near‐field and far‐field non‐circular sources, a special cumulant is used to eliminate the range of the mixed near‐field and far‐field non‐circular sources and a one‐dimensional (1‐D) spectral search is used to obtain all the directions of arrival, and the range can be obtained by defining the range spectrum; finally, numerical simulation results demonstrate the superiority of the proposed array.

In order to solve the problem of severely decreased performance under the situation of rapid moving sources and unstable array platforms, a null broadening robust adaptive beamforming algorithm based on power estimation is proposed in this paper. First of all, we estimate the interference signal power according to the characteristic subspace theory. Then, the correspondence between the signal power and steering vector (SV) is obtained based on the orthogonal property, and the interference covariance matrix (ICM) is reconstructed. Finally, with the aim of setting virtual interference sources, null broadening can be carried out. The proposed algorithm results in a deeper null, lower side lobes and higher tolerance of the desired signal steering vector mismatch under the condition of low complexity. The simulation results show that the algorithm also has stronger robustness.

Direction-of-arrival (DOA) in coprime linear array involves the problem that existing algorithms have less taken signal features into consideration. Considering that there is coexistence of multiple both circular and strictly non-circular signals in communication systems, an approach to DOA estimation of high accuracy and degree of freedom is proposed in this letter. We first use the ellipse covariance matrix and matrix completion method to reconstruct the covariance matrix of a virtual linear array. This virtual matrix only concludes the components of noncircular signals. Then, we similarly use the covariance matrix and matrix completion method to reconstruct a new covariance matrix including all signals of another virtual linear array. We use the virtual non-circular covariance matrix to eliminate the non-circular components in another virtual matrix. Finally, this matrix only concludes the circular signals. And we apply multiple signals classification to obtain the DOAs of two kinds of signals separately. Simulation results show that the proposed algorithm can obtain a highest degree of freedom than existing algorithms and hold on a preferable accuracy.