The performance of traditional direction of arrival (DOA) estimation algorithm based on uniform circular array (UCA) is constrained by the array aperture. Furthermore, the array requires more antenna elements than targets, which will increase the size and weight of the device and cause higher energy loss. In order to solve these issues, a novel low energy algorithm utilizing array base-line rotation for multiple targets estimation is proposed. By rotating two elements and setting a fixed time delay, even the number of elements is selected to form a virtual UCA. Then, the received data of signals will be sampled at multiple positions, which improves the array elements utilization greatly. 2D-DOA estimation of the rotation array is accomplished via multiple signal classification (MUSIC) algorithms. Finally, the Cramer-Rao bound (CRB) is derived and simulation results verified the effectiveness of the proposed algorithm with high resolution and estimation accuracy performance. Besides, because of the significant reduction of array elements number, the array antennas system is much simpler and less complex than traditional array.

In this paper, a low computational complexity rank reduce (RARE) algorithm based on 2-D nested array is proposed for direction of arrival (DOA) estimation in massive multiple-input multiple-output system. By utilizing the difference co-array of the 2-D nested array, one can obtain O(N ² ) virtual sensors with only N actual sensors, which can enable us to detect more number of sources than sensors. However, the large number of sensors would lead to huge computation burden, such as 2-D spectrum searching, for DOAs estimation of user's equipment. To overcome this problem, the RARE algorithm enjoying simple implementation is derived based on 2-D nested array. Instead of finding the solutions in 2-D observation space, the proposed method can solve the problem with two 1-D functions, which would reduce computational burden dramatically. Then by extending the original manifold to a larger set in the observation space, it would be much easier to obtain the solutions. Besides, the estimated azimuth and elevation angles can be paired automatically. The simulation results demonstrate the validity of the proposed method.

Recent developments in electronic countermeasures have heightened the demand for direction of arrival (DOA) estimation, and it has become more urgent to improve the resolution of closely spaced sources for conformal arrays. In previous studies, distributed sources incident on linear or planar arrays have been the point of focus, and their performance for closely spaced sources deteriorates sharply. To address this issue, we design a multi-head attention (MHA) mechanism-based cascaded estimator, which is composed of a signal-to-noise ratio (SNR) classification network and DOA estimation networks. The former network utilizes fully connected neural networks for SNR classification, and its output can activate the corresponding estimating subnetworks. In the estimating subnetworks, we adopt multiple self-attention mechanisms for the first time to extract more implied and representative information from original features for effective estimation. Numerical results validate that the proposed estimator not only exhibits better performance in terms of resolution and recovery accuracy even at lower SNR, but also provides great robustness with respect to angle intervals and snapshots, which lays the groundwork for further DOA research for closely spaced sources.

In this paper, a novel 2D direction of arrival (DOA) estimation approach based on tensor technique for the conformal array is proposed. In order to improve the estimation performance with low SNR and small snapshots, a third-order measurement tensor is constructed by exploiting the multidimensional information of the conformal array received data. Then, the desired signal subspace is obtained by the higher-order singular value decomposition (HOSVD) of the cross covariance tensor. Finally, the DOAs could be estimated by the conventional subspace based algorithms. This approach enables us to get a better DOA estimation performance owing to utilize the multidimensional information and the cross covariance tensor method, especially in the case of low SNR and small number of snapshots. At last, the simulation results demonstrate the validity and the advantages of the proposed method.

A multi-path routing algorithm based on network coding is proposed for combating long propagation delay and high bit error rate of space information networks. On the basis of traditional multi-path routing, the algorithm uses a random linear network coding strategy to code data packets. Code number is determined by the next hop link status and the number of current received packets sent by the upstream node together. The algorithm improves retransmission and cache mechanisms through using redundancy caused by network coding. Meanwhile, the algorithm also adopts the flow distribution strategy based on time delay to balance network load. Simulation results show that the proposed routing algorithm can effectively improve packet delivery rate, reduce packet delay, and enhance network performance

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.