In this letter, we proposed a novel unsupervised learning strategy for direction-of-arrival (DOA) estimation network. Inspired by the sparse power spectrum and $\ell_1$ -norm optimization, we develop a novel loss function to cooperate with the estimation network. Unlike the prior DL-based methods, the proposed method does not need any manual annotations for training and validation datasets. Compared with state-of-art methods, the proposed method can automatically increase the degree of freedom of the array without further pre-processing on the covariance matrix of array observation data. Moreover, the proposed method can obtain clear spectrum and precise DOAs under harsh estimation environments without any time-consuming problem.

Due to the variable curvature of the conformal carrier, the pattern of each element has a different direction. The traditional method of analyzing the conformal array is to use the Euler rotation angle and its matrix representation. However, it is computationally demanding especially for irregular array structures. In this paper, we present a novel algorithm by combining the geometric algebra with Multiple Signal Classification (MUSIC), termed as GA-MUSIC, to solve the direction of arrival (DOA) for cylindrical conformal array. And on this basis, we derive the pattern and array manifold. Compared with the existing algorithms, our proposed one avoids the cumbersome matrix transformations and largely decreases the computational complexity. The simulation results verify the effectiveness of the proposed method.

In view of the polarisation diversity of conformal array antennas, the cylindrical conformal array manifold model based on geometric algebra is established. With the help of special element arrangement, the spatial smoothing technology of coherent non‐circular signals for cylindrical conformal array, termed as GASS, is derived. Compared with the conventional analytic methods based on Euler angle, the proposed algorithm avoids the cumbersome matrix transformations and, therefore, has a much lower computational complexity. In addition, since the geometric algebra is introduced in the process of the array modelling, the presented algorithm is not limited to any specific array geometry. Monte‐Carlo simulation results are provided to demonstrate the effectiveness of the proposed method.

In this paper, the problem of direction of arrival (DOA) estimation is considered in the case of multiple polarized signals impinging on the conformal electromagnetic vector-sensor array (CVA). We focus on modeling the manifold holistically by a new mathematical tool called geometric algebra. Compared with existing methods, the presented one has two main advantages. Firstly, it acquires higher resolution by preserving the orthogonality of the signal components. Secondly, it avoids the cumbersome matrix operations while performing the coordinate transformations, and therefore, has a much lower computational complexity. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

The two-dimensional (2D) direction-of-arrival (DOA) estimation problem for noncircular signals using quaternions is considered in this paper. In the framework of quaternions, we reconstruct the conjugate augmented output vector which reduces the dimension of covariance matrix. Compared with existing methods, the proposed one has two main advantages. Firstly, the estimation accuracy is higher since quaternions have stronger orthogonality. Secondly, the dimension of covariance matrix is reduced by half which decreases the computational complexity. Simulation results are presented verifying the efficacy of the algorithm.

As application complexity continues to grow, using multicore processors has been proved to be an effective methodology to meet the ever-increasing processing demand across the industry association. The Master/Slave model, the Data Flow model and the OpenMP model are the three dominant models for parallel programming. In this paper, the first two models are briefly discussed while the OpenMP model is focused. Some factors (e.g. the number of threads, the scheduling strategy, the load balance, etc.) that affecting the execution performance of OpenMP programs were also studied in this paper. This paper presents a method of taking advantage of the OpenMP model to realize the image edge detection within the platform of TMS320C6678 DSP. The experimental results show that the OpenMP model has a better advantage on scalability and flexibility compared to the Master/Slave model and the Data Flow model. The best performance can be obtained when the number of threads is equal to the number of cores which are available within the platform. Under the circumstance of using the eight cores of TMS320C6678 DSP simultaneously, an image of 1024×768 pixels just needs 6.192ms to complete the edge detection. This result is impressive compared to the Master/Slave model's which saves 32.10% in time. Further more, if we use 1 to 8 cores, the respective execution time reduces resulting in the speedup approximately conforms to the Gustafson's law. In the case of 8 cores, the speedup reaches 7.233.