Achieving joint communication and imaging via uplink transmission presents significant challenges due to the unknown communication signal and the coupling of communication and sensing echoes. In this paper, a joint uplink communication and imaging system with only one RF chain is proposed, where a reconfigurable intelligent surface (RIS) is used to assist the base station (BS) to achieve joint signal detection and imaging (JSDI). Aiming to enhance the transmission gain in the desired directions and generate the required radiation pattern in the imaging region of interest (RoI), a RIS phase optimization problem is formulated, which is high dimensional and non-convex. We transform the original problem into a more tractable form by introducing auxiliary variables. Then, a backpropagation (BP) based Batch Gradient Descent (BGD) for both continuous and discrete phase cases is developed. Numerical results show that the proposed RIS phase optimization method achieves a controllable trade-off between communication and imaging performance and reveals a favorable range of the weighting factor where imaging performance can be significantly improved without causing a severe loss in symbol detection. Additionally, the echo decoupling problem is tackled using a Bayesian approach with factor graph techniques, which involve joint maximum a posteriori (MAP) probability estimation and adaptive sparse Bayesian learning (SBL). The proposed decoupling method asymptotically approaches the lower bound of communication and imaging. Numerical results also show that communication performance can be enhanced by utilizing imaging echoes compared to other benchmark communication systems.

Understanding embedding relationships is crucial for neural network interpretability, structural analysis, and data exploration. However, visually comparing embeddings is challenging due to the difficulty of mentally aligning structures across views. In this paper, we address this challenge by constructing multiple hierarchies of data points from different perspectives to facilitate meaningful comparisons. We introduce Parallel Clusters (ParaClus), a visual analytics system that enables multi-scale exploration of embedding structures. This is achieved through cluster formations across embeddings and attributes, along with an adaptive thresholding mechanism that defines neighborhoods. By dynamically adjusting this threshold, users can explore structures at varying scales. Our interface employs a parallel axes design, where clusters derived from the same embedding or attribute are aligned along individual axes. This layout allows users to easily compare neighboring clusters and observe relationships across embeddings. Furthermore, interactive subdivision mechanisms enable users to refine clusters based on their connections to other clusters, providing deeper insights into structural dependencies. Additionally, our system seamlessly integrates labels and scalar attributes into the clustering process, offering a unified approach to analyzing multi-attribute, time-varying, and network-derived embeddings. We evaluate the effectiveness of ParaClus through expert assessments and case studies.

For base stations (BSs) with integrated sensing and communication (ISAC) capabilities, interference from neighboring BSs not only degrades their communication performance but also increases sensing errors. While beamforming is commonly employed to suppress interference from undesired directions, this approach overlooks the potential sensing gain offered by target-reflected interference signals. In this paper, we investigate a scenario where a BS employs adaptive beamforming to leverage target-reflected interference for enhanced sensing performance. Notably, we deliberately preserve the line-of-sight interference component to enable accurate estimate of the interference symbols, enabling interference utilization without prior knowledge of the pilot of interference signals. Simulation results validate the effectiveness of the proposed approach in improving sensing performance through beamforming-enabled exploitation of interference.

In distributed airborne radar systems, the linear target localization method using time of arrival (TOA) is extensively studied due to its excellent robustness and simplicity in computation. Quadratic or higher-order terms related to range are disregarded in conventional linear methods, which results in compromised localization accuracy under high noise conditions. Considering the position uncertainty of moving platforms and a more realistic prior variance in target position estimation, an improved planar approximation algorithm is proposed, which refines target location iteratively. The spherical equations are approximated in each iteration as planar equations involving the current estimated target position. The error introduced by the planar approximation is recalculated and compensated for, enabling high-accuracy target location. In simulations, the proposed algorithm can reduce the average number of iterations by approximately 20%, and achieves a localization accuracy improvement of over 5 dB when the standard deviation of distance measurement error exceeds 1 km.

Mitigation of interference between base stations (BSs) is a significant challenge in integrated sensing and communication (ISAC) systems, particularly in noncooperative deployments. This letter investigates the scenario where an ISAC-enabled BS experiences interference from downlink (DL) transmission of another noncooperative BS (NBS). We observe that target-reflected interference contains valuable information, motivating its exploitation to enhance sensing capability. However, precise symbol estimation of NBS signals is infeasible without pilot information. To address this, we propose a novel iterative reconstruction-elimination algorithm (IREA) that derives a phase-ambiguous estimate of NBS signals through an efficient one-dimensional search, thereby enabling both interference mitigation and target information extraction from the reflected interference signals. Simulations demonstrate significant improvements in target detection and localization performance through our interference exploitation method.

Ordering has been extensively studied in many visualization applications, such as axis and matrix reordering, for the simple reason that the order will greatly impact the perceived pattern of data. Many quality metrics concerning data pattern, perception, and aesthetics are proposed, and respective optimization algorithms are developed. However, the optimization problems related to ordering are often difficult to solve (e.g., TSP is NP-complete), and developing specialized optimization algorithms is costly. In this paper, we propose Versatile Ordering Network (VON), which automatically learns the strategy to order given a quality metric. VON uses the quality metric to evaluate its solutions, and leverages reinforcement learning with a greedy rollout baseline to improve itself. This keeps the metric transparent and allows VON to optimize over different metrics. Additionally, VON uses the attention mechanism to collect information across scales and reposition the data points with respect to the current context. This allows VONs to deal with data points following different distributions. We examine the effectiveness of VON under different usage scenarios and metrics. The results demonstrate that VON can produce comparable results to specialized solvers.

Recent research shows that distributed radar has great potential in recognizing and countering deception jamming. In this letter, we investigate how to use deception bistatic range measurements to estimate the target location and deception ranges, and propose a message passing based method for target localization under range deception jamming in distributed MIMO radar. Firstly, the a posteriori distribution of the target location is derived, but its maximization is intractable. Then we represent the joint distribution of the relevant variables as a factor graph model and a highly efficient message passing algorithm is developed, where the target location and deception ranges are estimated iteratively. The Cramer-Rao bound is also derived and numerical simulations are provided to demonstrate the superiority of the proposed method.

In this letter, a novel factor graph approach to target localization in distributed MIMO radars is proposed. To achieve robust localization in the presence of outliers, target localization can be formulated as a least absolute deviation (LAD) problem, which, however, is difficult to solve. We then reformulate the LAD problem as a reweighted least square (LS) one, which is converted to a product of some functions, enabling the use of factor graph techniques. Based on a factor graph representation, a highly efficient message passing algorithm is developed, where the target location is estimated in an iterative way. Comparisons with state-of-the-art methods show that the proposed method is superior in terms of computational complexity, robustness and accuracy.