Explore the vast range of titles available.

Identification of bird species from audio recordings has been a major area of interest within the field of ecological surveillance and biodiversity conservation. Previous studies have successfully identified bird species from given recordings. However, most of these studies are only adaptive to low‐noise acoustic environments and the cases where each recording contains only one bird's sound simultaneously. In reality, bird audios recorded in the wild often contain overlapping signals, such as bird dawn chorus, which makes audio feature extraction and accurate classification extremely difficult. This study is the first to focus on applying a blind source separation method to identify all foreground bird species contained in overlapping vocalization recordings. The proposed IVA‐Xception model is based on independent vector analysis and convolutional neural network. Experiments on 2020 Bird Sound Recognition in Complex Acoustic Environments competition (BirdCLEF2020) dataset show that this model could achieve a higher macro F1‐score and average accuracy compared with state‐of‐the‐art methods.

Most existing Bayesian compressive sensing (BCS) algorithms are developed in real numbers. This results in many difficulties in applying BCS to solve complex‐valued problems. To overcome this limitation, this letter extends the existing real‐valued BCS framework to the complex‐valued BCS framework. Within this framework, the multitask learning setting, where L tasks are statistically interrelated and share the same prior, is considered. It is verified by numerical examples that the developed complex multitask compressive sensing (CMCS) algorithm is more accurate and effective than the existing algorithms for the complex sparse signal reconstructions

This paper proposes a modified fast off‐grid L1‐SVD (M‐FOGL1SVD) method for direction of arrival (DOA) estimation. Unlike FOGL1SVD, after obtaining the positions of the nonzero rows of the signal sources, the off‐grid overcomplete basis matrix is used to update the signal sources, thus improving the estimation accuracy of it. In addition, to reduce the approximate error of the first‐order off‐grid model, a second‐order off‐grid model is introduced through a further Taylor expansion of the steering vector. Finally, the formula for solving the off‐grid gap under the novel model is derived. Extensive simulation results indicate that the proposed algorithm has better performance than FOGL1SVD in terms of DOA estimation precision.

Recently, sparse arrays have received considerable attention as they provide larger array aperture and increased degrees‐of‐freedom (DOFs) compared to uniform linear arrays. These features are essential to enhance the direction‐of‐arrival estimation performance. However, most of the existing sparse arrays are mainly designed for circular sources and realize limited increment in DOFs for non‐circular sources. In this letter, a new sparse array configuration for non‐circular sources is presented, which significantly increases the achievable DOFs and improves the direction‐of‐arrival estimation performance. The proposed geometry comprises two effectively configured uniform linear arrays that exploit the characteristics of non‐circular sources and extend the array aperture. For a given number of sensors, its virtual array is advantageously a hole‐free uniform linear array. Moreover, the precise sensor locations, achievable DOFs, and optimal distribution of physical sensors are determined analytically by closed‐form expressions. Owing to these benefits, the proposed array efficiently resolve multiple sources in under‐determined conditions and achieves better direction‐of‐arrival estimation performance than its counterpart structures. Simulation results validate the superiority of the proposed configuration.

A design method of non‐linear frequency diverse array is presented to generate the desired direction modulation for the array. The proposed method firstly sets the desired direction modulation, and then forms the desired transmit beampattern by adjusting the frequency of each array element in a non‐linear manner. Theoretical analysis and numerical experiments have verified the effectiveness of the proposed method.

In this paper, we consider the reconfigurable intelligent surface (RIS)‐aided MIMO systems to realize a high quality link between the base station (BS) and the users via a RIS. To achieve this goal, the active beamforming at the BS and the passive beamforming at the RIS should be jointly optimized. However, most of the existing joint optimization schemes that maximize the sum‐rate have high computational complexity due to the complex derivative calculations and matrix inversion. To this end, a biologically inspired particle swarm optimization (PSO) algorithm is exploited to solve the non‐convex sum‐rate optimization problem with low‐complexity. Simulation results show that the proposed scheme provides a better trade‐off between performance and complexity than existing solutions for RIS‐aided MIMO systems.

When there is the coexistence of uncorrelated and coherent signals in sparse arrays, the conventional algorithms using coarray are fail. In order to solve this problem, the letter proposes a novel method based on compressed sensing. Firstly, the authors vectorize the covariance matrix and establish a sparse representation model through constructing a two‐dimensional redundant dictionary. Then, the authors use an improved orthogonal matching pursuit algorithm for off‐grid sources to recover the sparse vector. Through analysing location of non‐zero elements in sparse vector, the direction‐of‐arrivals of both uncorrelated and coherent signals can be obtained. The proposed method has no strict limitation by the structure of the existing sparse arrays. Moreover, it makes full use of vectorized data and can estimate more number of signals than that of sensors. Numerical experiments prove the effectiveness and favourable performance of the proposed method.

In practical applications, communication emitters are often carried by low orbit satellites aircraft, ships, and other moving carriers. Doppler phenomenon usually exists due to the relative motion between the emitter and the receiver, which seriously affects the identification performance. Given this situation, a method based on the signal vector decomposition and the multi‐feature fusion is proposed. By normalizing and demodulating the target signal, the signal vector diagram is decomposed into four parts. We present a multi‐feature fusion model with varying features extracted from each diagram by a deep learning network. Experimental results show that the proposed method is effective in the presence of Doppler effect.

This letter considers the coherent integration and detection for hypersonic target in the first‐order approximate (FOA) echo model. The proposed algorithm first accomplishes the modified pulse compression utilising the designed reference signal, then conducts the Keystone transform (KT) based coherent integration method to detect the target. Compared with the existing methods which model the received signal without considering the target's intrapulse motion, the proposed method has a lower modeling error and can maintain good noise robustness for weak hypersonic target detection. Experimental results are given to show the effectiveness of the proposed method.

Reconfigurable intelligent surface (RIS)‐empowered communications is on the rise and is a promising technology envisioned to aid in 6G and beyond wireless communication networks. RISs can manipulate impinging waves through their electromagnetic elements enabling some sort of control over the wireless channel. The potential of RIS technology is explored to perform a sort of virtual equalization over‐the‐air for frequency‐selective channels, whereas equalization is generally conducted at either the transmitter or receiver in conventional communication systems. Specifically, using an RIS, the frequency‐selective channel from the transmitter to the RIS is transformed to a frequency‐flat channel through elimination of inter‐symbol interference (ISI) components at the receiver. ISI is eliminated by adjusting the phases of impinging signals particularly to maximize the incoming signal of the strongest tap. First, a general end‐to‐end system model is provided and a continuous to discrete‐time signal model is presented. Subsequently, a probabilistic analysis for elimination of ISI terms is conducted and reinforced with computer simulations. Furthermore, a theoretical error probability analysis is performed along with computer simulations. It is analysed and demonstrated that conventional RIS phase alignment methods can successfully eliminate ISI, and the RIS‐aided communication channel can be converted from frequency‐selective to frequency‐flat.