In recent years, direct position determination (DPD) with multiple arrays for non-circular (NC) signals is a hot topic to research. Conventional DPD techniques with spectral peak search methods have high computational complexity and are sensitive to the locations of the observation stations. Besides, there will be loss when the signal propagates in the air, which leads to different received signal-to-noise ratios (SNRs) for each observation station. To attack the problems mentioned above, this paper derives direct position determination of non-circular sources for multiple arrays via weighted Euler estimating signal parameters viarotational invariance techniques (ESPRIT) data fusion (NC-Euler-WESPRIT) method. Firstly, elliptic covariance information of NC signals and Euler transformation are used to extend the received signal. Secondly, ESPRIT is applied to avoid the high-dimensional spectral function search problem of each observation station. Then, we combine the information of all observation stations to construct a spectral function without complex multiplication to reduce the computational complexity. Finally, the data of each observation station is weighted to compensate for the projection error. The consequence of simulation indicates that the proposed NC-Euler-WESPRIT algorithm not only improves the estimation performance, but also greatly reduces the computational complexity compared with subspace data fusion (SDF) technology and NC-ESPRIT algorithm.

Direct position determination (DPD) for augmented coprime arrays is investigated in this paper. Augmented coprime array expands degree of freedom and array aperture and improves positioning accuracy. Because of poor stability and noise sensitivity of the subspace data fusion (SDF) method, we propose two weighted subspace data fusion (W-SDF) algorithms for direct position determination. Simulation results show that two W-SDF algorithms have a prominent promotion in positioning accuracy than SDF, Capon, and propagator method (PM) algorithm for augmented coprime arrays. SDF based on optimal weighting (OW-SDF) is slightly better than SDF based on SNR weighting (SW-SDF) in positioning accuracy. The performance for DPD of the W-SDF method with augmented coprime arrays is better than that of the W-SDF method with uniform arrays.

Direct position determination (DPD) of noncircular (NC) sources with multiple nested arrays (NA) is investigated in this paper. Noncircular sources are used to expand the dimension of the received signal matrix, so the number of identifiable information sources and the accuracy of direct position determination are improved. Furthermore, nested array increases spatial degree of freedom. In this paper, the high‐dimensional search problem of noncircular sources is investigated. Therefore, we propose algorithm dimension reduction subspace data fusion (RD‐SDF) to reduce complexity and increase positioning accuracy. Simulation results show that the proposed RD‐SDF algorithm for multiple nested arrays with noncircular sources has improved positioning accuracy with higher spatial degree of freedom than SDF, Capon, and two‐step algorithms with uniform linear array and circular sources (CS).

In this paper, Direction of arrival estimation (DOA) in coprime array is investigated, and an advanced method for DOA estimation is presented with extended coprime array (ECA). Specifically, we use Discrete Fourier Transform (DFT) to obtain initial DOA estimation with large difference co-arrays generated by ECA, then perform Taylor series approximation and total least squares (TLS) method around the initial estimation to compensate the estimation offset. Compared to conventional DFT method, the proposed method enjoys better estimation accuracy with fewer computational burden since it avoids extra searches and benefits more from large difference co-array. Besides, in contrast to spatial smoothing based methods which halve array aperture, the proposed method avoids the process of spatial smoothing and fully utilizes uniform degrees of freedom (DOFs). In this case, the proposed method has advantages on estimation accuracy and computational efficiency with multiple impinging signals, especially under the condition of large difference co-array. Furthermore, the proposed method is applicable to other types of advanced coprime array. The superiority of the proposed method has been proved by numerical simulations.

Direction of arrival (DOA) estimation for non-Gaussian signals using three-level nested array (THL-NA) is investigated in this paper. Motivation from larger consecutive degree of freedom (DOF) and array aperture, the THL-NA is proposed, which can take full advantages of the consecutive coarrays of TL-NA and has the closed-form expression of DOF. Specifically, firstly, the array aperture is expanded by the second order sum coarray (2-SC) of the proposed array, secondly, the nested relationship between subarrays is employed to obtain the difference coarray of 2-SC (2-DCSC), finally, a consecutive virtual array with large array aperture is obtained. Besides, a successive SS-MUSIC algorithm is proposed, which employs the spatial smoothing estimating signal parameter via rotational invariance techniques (SS-ESPRIT) algorithm and partial spectrum searching multiple signal classification (PSS-MUSIC) to obtain initial estimations and fine estimations, respectively, resulting in a better balance between computational complexity and estimation accuracy.