No abstract

As we all know, the non‐uniform array is widely used in the mixed near‐field (NF) and far‐field sources localization. The nested array contains a uniform linear array, it is easy to be affected by mutual coupling, so we propose a symmetric thinned coprime array (STCA) to reduce the mutual coupling effect of mixed source localization. In this paper, the STCA configuration consists of two sparse uniform linear arrays, the first subarray is composed of 2N − 1 sensors, and the sensor element spacing is Md. The second subarray is composed of 2M+2⌊M2⌋ $2M+2\lfloor \frac{M}{2}\rfloor $ sensors, and the sensor element spacing are Nd and Md respectively. The two subarrays form the symmetric coprime array, which shares a reference sensor. Under the same number of physical array sensors, compared with the latest symmetric nested array, although STCA has lower consecutive lags, the array sensors have larger inter‐sensor spacing and larger physical array aperture, which can significantly reduce the mutual coupling effect between physical sensors, and better estimation performance of direction‐of‐arrival (DOA) estimation can be achieved. Finally, simulation results show that STCA can achieve better performance than other symmetric nested arrays under the same array sensors and mutual coupling effects.

At present, there are few sparse arrays used in the mixed near-field (NF) and far-field (FF) localization based on non-circular (NC) signals. Inspired by the symmetric flipped nested array (SFNA) used in the existing mixed NF and FF NC source, in order to further improve the parameter estimation accuracy of the mixed NF and FF NC signal, an improved symmetric flipped nested array (ISFNA) for mixed NF and FF NC sources localization was developed. First, the uniform subarrays in the SFNA are rearranged, ⌈ N 2 2⌉ − 1 elements are extracted from the uniform subarrays and rearranged into ISFNA. ISFNA is more sparse, the array aperture is larger, and the array degree of freedom (DOF) is higher; second, the formula of the maximum consecutive lags of ISFNA is given; third, a special fourth-order cumulant is used to eliminate the range parameter and then use a one-dimensional (1-D) spectral peak search to obtain all Directions of Arrival (DOAs). By defining the range search, the range can be obtained by bringing in estimated DOAs. Finally, the superiority of the proposed array is proved by simulation.

Owing to increased degrees of freedom (DOFs) and reduced mutual coupling effect, sparse linear arrays can improve the performance of direction‐of‐arrival estimation compared with uniform linear arrays. In this study, a novel sparse linear array is presented based on fourth‐order cumulants, which consists of five sparse sub‐arrays. The sensor locations of the five sub‐arrays are determined by a closed‐form expression. Closed‐form expressions of DOFs for the proposed array with any number of sensors are also derived. By optimising the allocation of sensors in each sub‐array, the DOFs of the proposed array with M sensors achieve scriptO()M4 $\mathcal{O}\left({M}^{4}\right)$. Because each sub‐array is sparse, the number of pairwise sensors with small separation is significantly alleviated. Theoretical analysis shows that the proposed array can provide a higher number of DOFs and is more robust in scenarios with heavy levels of mutual coupling than most sparse linear arrays. Numerical simulations are conducted to demonstrate the merits of the proposed array over other sparse linear arrays.

When the array structure of the sparse arrays (SA) cannot be determined, the existing beamforming algorithms designed according to specific formations such as coprime arrays (CA), nested arrays (NA), etc. will fail. To solve this problem, we propose two algorithms that are suitable for a variety of SA. In the first method, assuming that the desired signal is a non-Gaussian signal, the desired signal direction vector (DSDV) is estimated using the fourth-order cumulant, and then the interference plus noise covariance matrix (INCM) is reconstructed by integrating the area outside the desired signal. When the desired signal is a Gaussian signal, we propose the second method. The second method estimates the power and direction of arrival (DOA) of the signals by performing eigenvalue decomposition on the sampled covariance matrix (SCM) and finally calculates the weight vector. However, this method needs to estimate the DOA of the signals, so it has certain requirements for the SA structure design. The simulation results show that the proposed method has good performance and strong robustness under different SA.