Direction-of-arrival (DOA) estimation in coprime array involves the problem that there is the coexistence of both uncorrelated signals and coherent signals in multipath environment. An approach to estimate DOA of uncorrelated and coherent signals separately, based on coprime linear array and coprime planar array, is proposed in this paper. The uncorrelated signals are estimated first, where the root multiple signal classification (root-MUSIC) is applied in coprime linear array and unitary estimating signal parameters via rotational invariance techniques (Unitary-ESPRIT) is utilized in coprime planar array. Those subspace algorithms are of low complexity compared with the spectral peak search method. We then eliminate the components of noises and uncorrelated signals and reconstruct a covariance matrix of coherent signals. At last, we use the root-MUSIC or Unitary-ESPRIT to resolve one-dimensional or two-dimensional DOAs of coherent signals, respectively. Simulation results demonstrate the computational efficiency and high accuracy of the proposed algorithm. And results also prove that this algorithm can estimate the number of signals more than that of subarray sensors and separate two signals from the same angle.

The spectral minutiae representation (SMC) has been recently proposed as a novel method to minutiae‐based fingerprint recognition, which is invariant to minutiae translation and rotation and presents low computational complexity. As high‐resolution palmprint recognition is also mainly based on minutiae sets, SMC has been applied to palmprints and used in full‐to‐full palmprint matching. However, the performance of that approach was still limited. As one of the main reasons for this is the much bigger size of a palmprint compared with a fingerprint, the authors propose a division of the palmprint into smaller regions. Then, to further improve the performance of spectral minutiae‐based palmprint matching, in this work the authors present anatomically inspired regional fusion while using SMC for palmprints. Firstly, the authors consider three regions of the palm, namely interdigital, thenar and hypothenar, which have inspiration in anatomic cues. Then, the authors apply SMC to region‐to‐region palmprint comparison and study regional discriminability when using the method. After that, the authors implement regional fusion at score level by combining the scores of different regional comparisons in the palm with two fusion methods, that is, sum rule and logistic regression. The authors evaluate region‐to‐region comparison and regional fusion based on spectral minutiae matching on a public high‐resolution palmprint database, THUPALMLAB. Both manual segmentation and automatic segmentation are performed to obtain the three palm regions for each palm. Essentially using the complex SMC, the authors obtain results on region‐to‐region comparison which show that the hypothenar and interdigital regions outperform the thenar region. More importantly, the authors achieve significant performance improvements by regional fusion using regions segmented both manually and automatically. One main advantage of the approach the authors took is that human examiners can segment the palm into the three regions without prior knowledge of the system, which makes the segmentation process easy to be incorporated in protocols such as in forensic science.

Joint time of arrival and angle of arrival estimation based on a uniform circle array (UCA) involves problems including high computational complexity in the multi-dimensional spectral search method and lack of applicability in coherent multipath environments. In this paper, we introduce a fast estimation algorithm based on the orthogonal frequency division multiplexing system that uses space-frequency characteristics to reconstruct the virtual array and extend the array aperture. First, we combine the array structure with the signal frequency features to construct the extended virtual array. We then calculate the channel frequency response covariance matrix via frequency-domain smoothing preprocessing and determine a closed-form solution of angle of arrival using UCA estimating signal parameters via rotational invariance techniques. Finally, using the estimated angle values, we perform a one-dimensional spectral search to determine the time of arrival values. The simulation results indicate that the proposed method provides accurate estimation under the low signal-to-noise ratio conditions in coherent, independent or mixed multipath environments, and provides better performance than the multi-dimensional spectral peak search method, in terms of both computational complexity and estimation accuracy.

Direct position determination (DPD) is currently a hot topic in wireless localization research as it is more accurate than traditional two-step positioning. However, current DPD algorithms are all based on uniform arrays, which have an insufficient degree of freedom and limited estimation accuracy. To improve the DPD accuracy, this paper introduces a coprime array to the position model of multiple non-circular sources with a moving array. To maximize the advantages of this coprime array, we reconstruct the covariance matrix by vectorization, apply a spatial smoothing technique, and converge the subspace data from each measuring position to establish the cost function. Finally, we obtain the position coordinates of the multiple non-circular sources. The complexity of the proposed method is computed and compared with that of other methods, and the Cramer–Rao lower bound of DPD for multiple sources with a moving coprime array, is derived. Theoretical analysis and simulation results show that the proposed algorithm is not only applicable to circular sources, but can also improve the positioning accuracy of non-circular sources. Compared with existing two-step positioning algorithms and DPD algorithms based on uniform linear arrays, the proposed technique offers a significant improvement in positioning accuracy with a slight increase in complexity.

The existing coprime array is mainly applicable to circular sources, while the virtual array degree of freedom (DOF) for noncircular sources is enhanced limitedly. In order to perfect the array DOF and the direction of arrival (DOA) estimation accuracy, a high degree of freedom sparse array design method for noncircular sources is put forward. Firstly, the method takes the advantages of the characteristic of the noncircular sources to expand the array manifold and then explores and solves the location distribution of the physical array sensors on the basis of the virtual array model with the help of the searching approach. The array configuration can obtain the longest continuous virtual array. The comparisons between the proposed array configuration and the common array configurations are advanced. The simulation experiments show that the sparse array presented in this paper can effectively increase the continuous virtual array aperture of noncircular sources, improve the array DOF and DOA estimation accuracy, and achieve the purpose of better estimation of multiple DOAs in underdetermined conditions.

Recently, the design of sparse linear array for direction of arrival (DOA) estimation of non-circular (NC) signals has attracted great attention because the difference and sum co-array offered by non-circularity increases the aperture of virtual linear array. In this paper, we present a coprime array with shifted and flipped subarray for the DOA of non-circular signals. By shifting one subarray, the proposed array structure achieves a higher number of consecutive lags than the prototype coprime array with the same number of sensors. Then, through flipping the shifted subarray with the zero point as the symmetry point, the number of sensor pairs with small separation is significantly reduced, making the resulting structure much sparser. For the proposed array structure, we derive the closed-form expression for the number of consecutive lags, the optimal distribution of two subarrays with a given number of sensors and the weight function. Numerical simulations are conducted to verify the superiority of the proposed array over the existing sparse arrays.