A reconfigurable intelligent surface (RIS) assisted scheme is proposed for direction-of-arrival (DOA) estimation in this work. In the scheme, the RIS is utilized to provide a reflected path, and the RIS-reflected signal is received by array antennas together with the direct-path signal. The model consisting of the direct-path signal and the RIS-reflected signal is proposed, and the Cramer-Rao Bound (CRB) is derived based on the signal model to assess the ultimate estimation performance. In order to enhance the estimation performance of RIS-assisted scheme, two possible strategies for the phases design of RIS are presented. The first possibility considers RIS phase shifts that maximize the coherence of signals from the direct path and the RIS-reflected path. The other possibility considers to obtain the phase shifts that minimize the CRB of DOA estimation in the proposed scheme through a manifold optimization method. Moreover, DOA estimation methods are proposed for the RIS-assisted scheme to validate the scheme feasibility. Target tracking is proposed as a practical application for the RIS-assisted scheme. In the end, both theoretical analysis and simulation results illustrate that the accuracy of DOA estimation and target tracking has been improved with the aid of RIS.

Ultrasonic imaging is crucial in the fields of biomedical engineering for its deep penetration capabilities and non-ionizing nature. However, traditional techniques heavily rely on impedance differences within objects, resulting in poor contrast when imaging acoustically transparent targets. Here, we propose a compact spatial differentiator for underwater isotropic edge-enhanced imaging, which enhances the imaging contrast without the need for contrast agents or external physical fields. This design incorporates an amplitude meta-grating for linear transmission along the radial direction, combined with a phase meta-grating that utilizes focus and spiral phases with a first-order topological charge. Through theoretical analysis, numerical simulations, and experimental validation, we substantiate the effectiveness of our technique in distinguishing amplitude objects with isotropic edge enhancements. Importantly, this method also enables the accurate detection of both phase objects and artificial biological models. This breakthrough creates new opportunities for applications in medical diagnosis and nondestructive testing.

Multiple-input multiple-output (MIMO) has been used in wireless communications to increase data rates via multiplexing or improve performance via diversity. Additionally, in radar systems, MIMO promises improved parameter identifiability and target resolution. Frequency diverse (FD)-MIMO radar can effectively distinguish targets that are closely spaced in the same angle cell by exploiting its range-angle-dependent transmit beamform. Therefore, in this paper, we first propose an integrated waveform design by embedding weighted, phase-modulated communication signals in the FD-MIMO radar waveform. Then, we derive the Cramer-Rao lower bound (CRLB) of the location estimation and analyze the communication performance achieved when the communication receiver extracts information from the integrated signals. Next, transmit beamforming is optimized to achieve the best tradeoff between the radar and communication performances of the system. Due to the nonconvexity of the optimization problem, we apply sequential parametric convex approximation (SPCA) and semidefinite relaxation (SDR) methods to solve the problem. The simulation results reveal the effects of some parameters, such as the frequency interval, symbol period, and communication symbol sequence, on radar and communication performance and demonstrate the tradeoff between radar and communication obtained by optimizing the transmit beamforming vector.

Traditional radar signal sorting (RSS) methods assume that all types of radar signals can be stored and trained offline. However, the presence of non-cooperative radar emitters makes the assumption unrealistic in real-world RSS problems. This paper proposes a real-time method for RSS in complex electromagnetic environments based on the Bayesian framework. Each type of radar signal is regarded as a collection of samples from Gaussian distribution. Bayesian inference is applied to calculate the likelihood of a new data point belonging to a known category and the Chinese restaurant process (CRP) is adopted to provide some prior knowledge which helps calculate the probability of opening a new cluster. For the overclustering problem caused by dynamic and large-scale changes in pulse descriptor words (PDWs) of the signals from the same radar emitter, a dynamic cluster merging (DCM) algorithm is introduced. The Wasserstein distance and Hausdorff distance of the probability density functions (PDFs) of the PDWs between clusters are weighted, and the pulse repetition interval (PRI) is also considered to measure the similarity between clusters which helps merge clusters of the same type of signal. The method proposed in this paper is verified under the conditions of large-scale changes in PDWs and dynamic changes in signal numbers over time. Higher accuracy of real-time signal sorting is achieved on the simulated dataset of signals from 30 types of radar emitters compared with existing methods. Even in the situations with a significant pulse loss rate, the proposed algorithm still attains high accuracy.

In this paper, an iterative procedure for the synthesis of sparse arrays radiating focused beampattern is presented. The proposed approach provides a signifcant reduction in the complexity of the beam forming network, which is fulfilled by reducing the number of antenna elements in the array. An iterative scheme is used where the prescribed pattern response in the mainlobe is cast as a multi-convex problem at each step that the nonconvex lower bound constraint is relaxed while including a reweighted l1-norm minimization based on the magnitudes of the elements. Thus, a sparse array with fewer elements (compared to other methods) and a better performance of beam pattern (e.g., narrower 3-dB beamwidth, lower maximum sidelobe level) is produced. The resulting sparse array is able to generate a steerable pencil beam, matching a given power mask and avoid to constraint the fitting of any a priori defined reference beam pattern. The practical array imperfections are also compensated in the optimization stage by using worst-case performance optimization technique. Examples concerning the design of linear and planar arrays show relevant savings of array elements with respect to conventional array techniques.