This letter considers the joint design of transmit waveform and receive beamforming for low probability of intercept (LPI) of multiple-input multiple-output with frequency diverse array radar in cluttered environments. The goal is to maximize the output signal-to-interference-plus-noise ratio while controlling the transmit power at the target region which enhances LPI capability, subject to peak-to-average power ratio and similarity constraints. To tackle the formulated non-convex optimization problem, an alternating optimization algorithm based on the alternating direction method of multipliers is proposed. Simulation results are provided to validate the effectiveness of the developed method in terms of the objective function values as well as the transmit and receive beampatterns.

Low-rank tensor completion (LRTC) that aims to restore the latent clean data from an incomplete and/or degraded observation, shows promising results in ubiquitous tensorial data completion applications. Most tensor completion approaches are vulnerable to outliers since their derivations are based on $\ell _{2}$ -space to be robust against Gaussian noise. In this work, to tackle this issue, $\ell _{p}$ -regression $(0 < p < 2)$ is employed to achieve outlier resistance, where a factored form of tensor train (TT)-format representation is regularized by the low-TT-rank prior to exploit the inter-fibers correlation. On the basis of that, an effective iterative $\ell _{p}$ -regression TT completion method (referred to $\ell _{p}$ -TTC) is proposed, with the advantage of not requiring the hard-to-determine user-defined weights in TT rank model. Extensive experiment results are presented to demonstrate the outlier resistance of the proposed $\ell _{p}$ -TTC, and showing the effective and superior performance in both bistatic MIMO radar localization and color image inpainting and denoising, compared with state-of-the-art tensor completion approaches.

Remote sensing semantic segmentation is considered a key step in the intelligent interpretation of high-resolution remote sensing images, with widespread applications in fields such as hazard assessment, environmental monitoring, and urban planning. Recently, numerous deep learning-based semantic segmentation methods have emerged, achieving significant breakthroughs. However, the majority of current research still concentrates on representation learning in the visual feature space, with the potential of multimodal data sources yet to be fully explored. In recent years, the foundational visual language model, namely contrastive language-image pre-training (CLIP), has established a new paradigm in the visual field, demonstrating excellent generalization capabilities and deep semantic understanding across a variety of tasks. Inspired by prompt learning, we propose a prompting approach based on linguistic descriptions to enable CLIP to generate semantically distinct contextual information for remote sensing images. We introduce the SegCLIP network architecture, a novel framework specifically designed for semantic segmentation of high-resolution remote sensing images. Specifically, we have adapted CLIP to extract text information, thereby guiding the visual model in distinguishing among classes. Additionally, we have designed a cross-modal feature fusion module that integrates linguistic and visual semantic features, ensuring semantic consistency across modalities. Finally, we have fully exploited the potential of text data and have used additional real text to refine ambiguous query features. Experimental evaluations confirm that the method exhibits superior performance on the LoveDA, iSAID, and UAVid public semantic segmentation datasets.

A subspace‐based method is considered to deal with multiple near‐field source localisation with a uniform circular array. The property of the centro‐symmetric circular array configuration is employed and a matrix which is free of range is constructed. Then, the two‐dimensional (2D)‐MUSIC method is used to search the 2D angles after the range parameters are separated. After the 2D angles are estimated, a 1D‐MUSIC method is used to estimate the corresponding ranges when the estimated 2D angles are substituted into the original form of the received data. It is shown that the proposed method can deal with the case of multiple near‐field sources, but with lower computational complexity than that of the available 3D MUSIC method; moreover, the 3D parameters are automatically paired.