To address the challenges of ensuring communication security for legitimate users in the presence of eavesdroppers and the limited detection performance for extended targets in an Integrated Sensing and Communication (ISAC) system, this paper proposes a secure waveform design method for a multi-input multi-output (MIMO) ISAC system, under a stochastic Target Impulse Response (TIR) model. Firstly, To prevent confidential information in ISAC systems from being intercepted by unauthorized users, transmit waveforms and artificial noise(AN) are designed to maximize the sum secrecy rate of all communication users while ensuring individual user quality of service. Secondly, recognizing that accurate TIR information is unavailable in the practical detection environment, the stochastic TIR model is established under the assumption that prior errors follow a complex Gaussian distribution, and the robust detection probability of the extended target under the stochastic TIR is employed as a constraint to ensure the system’s detection performance. To solve this non-convex mixed max-min fractional programming(FP) problem, the Decomposition-Based Large Deviation Inequality (DBLDI) is adopted to convert the robust detection probability constraint into a convex constraint. By incorporating quadratic transform, inverse quadratic transform, and Lagrangian Dual Transform, a FP solution approach is developed to convert the mixed max-min FP problem into a convex problem. Finally, semidefinite optimization and alternating optimization methods are employed to iteratively optimize the transmit waveform, AN, and the receive filter. Additionally, the convergence and complexity analysis of the proposed method are provided. Simulation results demonstrate that the proposed method can guarantee the communication security of multiple legitimate users in the presence of multiple eavesdroppers. Meanwhile, it can also meet the stable detection requirements of extended targets, and the solution speed is nearly twice higher compared with the BTI method.

This work addresses the challenges of communication signal detection and direction of arrival (DOA) estimation in integrated sensing and communications (ISAC) systems with hardware imperfections. Conventional signal processing techniques often fail to effectively manage the complex nonlinearities caused by hardware imperfections, such as those introduced by power amplifiers and local oscillators. Recently, deep neural networks (DNNs) have been employed to mitigate the hardware imperfections, which however require a substantial amount of pilot signals for training, leading to unacceptable overhead and impracticality in fast time-varying channels. In this work, we employ an NN to characterize the nonlinear system, and propose a novel iterative approach to joint NN-based nonlinear system model learning, signal detection and DOA estimation. Instead of relying on pilot signals for NN learning, the proposed approach utilizes communication data signals as virtual training samples, enabling more accurate nonlinear model learning, which subsequently enhances signal detection and DOA estimation. A Bayesian framework is applied to the joint problem, wherein the NN parameters, the communication signals and the DOAs are jointly obtained by developing a message passing based inference algorithm. In particular, we impose sparse priors on the weights of the NN, so that overfitting can be better handled, resulting in significant improvement in system modeling performance. Extensive simulation results show that, compared to the state-of-the-art approaches, the proposed one delivers significantly better performance.

This paper mathematically models a constant-function automated market maker (CFAMM) position as a portfolio of exotic options, known as perpetual American continuous-installment (CI) options. This model replicates an AMM position's delta at each point in time over an infinite time horizon, thus taking into account the perpetual nature and optionality to withdraw of liquidity provision. This framework yields two key theoretical results: (a) It proves that the AMM's adverse-selection cost, loss-versus-rebalancing (LVR), is analytically identical to the continuous funding fees (the time value decay or theta) earned by the at-the-money CI option embedded in the replicating portfolio. (b) A special case of this model derives an AMM liquidity position's delta profile and boundaries that suffer approximately constant LVR, up to a bounded residual error, over an arbitrarily long forward window. Finally, the paper describes how the constant volatility parameter required by the perpetual option can be calibrated from the term structure of implied volatilities and estimates the errors for both implied volatility calibration and LVR residual error. Thus, this work provides a practical framework enabling liquidity providers to choose an AMM liquidity profile and price boundaries for an arbitrarily long, forward-looking time window where they can expect an approximately constant, price-independent LVR. The results establish a rigorous option-theoretic interpretation of AMMs and their LVR, and provide actionable guidance for liquidity providers in estimating future adverse-selection costs and optimizing position parameters.

In the case of distributed target detection in unknown Gaussian noise, training data are often limited, and signal mismatch is a common issue. To tackle these problems, we utilize the Bayesian theory by taking the noise covariance matrix as an inverse Wishart distribution. Our approach involves incorporating a fictitious determinist jamming signal in the signal-absence hypothesis to create a selective detector. Although this detector provides enhanced capability to reject mismatched signals, it comes at the cost of lower detection performance in the absence of signal mismatch. We propose a flexible Bayesian detector to tackle this limitation, wherein a customizable parameter can regulate the performance of mismatched signals. The tunable Bayesian detector is particularly robust to signal mismatch. In addition, it can achieve a higher probability of detection (PD) compared with existing methods when the tunable parameter is appropriately adjusted for matched signals. All the proposed Bayesian detectors can work with limited training data or even with no training data. The effectiveness of the proposed detectors is demonstrated through both simulated and actual data.

Remote sensing semantic segmentation is considered a key step in the intelligent interpretation of high-resolution remote sensing (HRRS) 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 pretraining (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 HRRS 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 (CFF) 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.

Abstract The existing deep learning‐based face super‐resolution techniques can achieve satisfactory performance. However, these methods often incur large computational costs, and deeper networks generate redundant features. Some lightweight reconstruction networks also present limited representation ability because they ignore the entire contour and fine texture of the face for the sake of efficiency. Here, the authors propose a convenient alternating projection network (CAPN) for efficient face super‐resolution. First, the authors design a novel alternating projection block cascaded convolutional neural network to alternately achieve content consistency and learn detailed facial feature differences between super‐resolution and ground‐truth face images. Second, the self‐correction mechanism enabled the convolutional layer to capture faithful features that facilitate adaptive reconstruction. Moreover, a convenient connection operation can reduce the generation of redundant facial features while maintaining accurate reconstruction information. Extensive experiments demonstrated that the proposed CAPN can effectively reduce the computational cost while achieving competitive qualitative and quantitative results compared to state‐of‐the‐art super‐resolution methods.

Abstract Multiple‐input multiple‐output with frequency diverse array (FDA‐MIMO) radar has drawn great attention due to providing the range‐angle beampattern via designing the transmit beamforming matrix. In this work, the authors investigate the transmit beamforming matrix optimization for Low‐Probability‐of‐Intercept (LPI) of FDA‐MIMO radar, which can accurately control the transmit beam energy to meet the LPI requirements and the clutter suppression. The idea of the transmit beamforming matrix design is to minimise the transmit power in a specific direction and simultaneously maximise the signal‐to‐interference‐plus‐noise ratio under the power constraint on each array element. To this end, a constrained multiple‐ratio fractional programming model with concerning the transmit beam matrix and the receive filter is first constructed, and then, it is transformed into two suboptimisation problems using a circular iterative approach. Moreover, the specific solution of the transmit beamforming matrix is obtained using the quadratic transformation method and the alternating direction method of multipliers algorithm. In addition, the computational complexity is also analysed in this paper. The simulation results demonstrate the correctness and effectiveness of the proposed method.

The structure and texture of images are crucial for remote sensing image super-resolution. Generative adversarial networks (GANs) recover image details through adversarial training. However, the recovered images always have structural distortions on the one hand, and GANs are difficult to train on the other hand. In addition, some methods assist reconstruction by introducing prior information of the image, but this brings additional computational cost. To address this issue, we propose a novel structure-texture parallel embedding (SPE) method for super-resolution (SR) of remote sensing images. Our method does not require additional image priors to reconstruct high-quality images. Specifically, we use the global structure information and local texture information of the image in the ascending space to guide the reconstruction result of the image. Firstly, we design a structure preserving block (SPB) to extract global structural features in the ascending space of the image, so as to obtain global structure information for a priori representation. Then, we design a local texture attention module (LTAM) to restore richer texture details. We have conducted lots of experiments on Draper public dataset. Experimental results show that our proposed method not only achieves a better trade-off between computational cost and performance, but also outperforms the existing several SR methods in terms of objective index evaluation and subjective visual effects.

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.

In this paper, the problem of joint multi-pitch and direction-of-arrival (DOA) estimation for multichannel harmonic sinusoidal signals is considered. A spatio-temporal matrix signal model for a uniform linear array is defined, and then the ESPRIT method based on subspace techniques that exploits the invariance property in the time domain is first used to estimate the multi pitch frequencies of multiple harmonic signals. Followed by the estimated pitch frequencies, the DOA estimations based on the ESPRIT method are also presented by using the shift invariance structure in the spatial domain. Compared to the existing state-of-the-art algorithms, the proposed method based on ESPRIT without 2-D searching is computationally more efficient but performs similarly. An asymptotic performance analysis of the DOA and pitch estimation of the proposed method are also presented. Finally, the effectiveness of the proposed method is illustrated on a synthetic signal as well as real-life recorded data.

In this paper, the problem of self-calibration for large astronomical arrays such as the Dutch Low Frequency Array (LOFAR) is considered. We assume direction dependent gain and phase errors which need to be estimated and calibrated out. Combining the subspace fitting and least square approaches, the signal subspace of the received single short-term interval (STI) sample data of the LOFAR is used to build a cost function whose minimizer is a statistically efficient estimator of the unknown parameters-the gains and phases of the telescopes. Subsequently, an iterative algorithm for finding the minimum of the cost function is presented and the unknown calibration parameters of both the core stations and the external subarray are separated. As a result, the computational complexity of the proposed method is significantly reduced compared to the existing methods based on a direct covariance fitting. Finally, the performance of the proposed method is compared with the conventional peeling method in computer simulation. An example for calibrating the core of the LOFAR array on Cyg A is also provided.

By applying a two-dimensional parameter estimation method proposed by M. Viberg and P. Stoica (see Conf. Rec. 32nd Asilomar Conf. Signals, Systems, Computers, vol.2, p.735-9, 1998), we develop a subspace method for estimating the differential delay of a sinusoidal signal received at two separated sensors as well as the sinusoidal frequencies. Using state-space realization, the time delay and frequency estimates are obtained from the state transition and observation matrices. Performance evaluation via computer simulations is included to demonstrate the effectiveness of the proposed algorithm.