Due to the large diving trajectory, the design of the imaging algorithm remains an exceptionally difficult task for hypersonic synthetic aperture radar (SAR). In this article, an accurate range model that accounts for the impact of higher-order motion factors on imaging performance is established. Based on the model, the signal characteristics are analyzed, which indicates that the design of the imaging approach faces greatly challenges such as severe cross-couplings and significant spatial variations (SVs). In view of these issues, a time–frequency hybrid domain imaging algorithm (TFHDIA) is proposed for hypersonic vehicle (HSV)-borne SAR systems with a large diving trajectory. The cross-couplings are significantly reduced by azimuth preprocessing. The 2-D SV in the range cell migration (RCM) and the first-order SV in the Doppler parameters (DPs) are eliminated through the fractional Fourier transform (FrFT) and the extended keystone transform (EKT). Additionally, the higher-order SVs of DPs are removed by the phase filter bank, which includes range, azimuth, and cross-coupling spatially variant phase error compensation filters. The proposed algorithm exhibits strong capabilities in eliminating SVs and effectively reducing cross-coupling, making it particularly suitable for HSV SAR systems with large diving trajectories. Simulation and actual acquired data results validate the efficacy of the proposed algorithm.

Compared with conventional synthetic aperture radar (SAR) systems, hypersonic vehicle-borne (HSV) SAR with a spiral trajectory offers unique advantages, including high maneuverability, large detection range, and diverse observation views, enabling precise three-dimensional (3-D) SAR coordinate positioning. However, the high maneuverability of the spiral trajectory poses significant challenges, such as model mismatch, image defocusing, and positioning algorithm failures. To address these challenges, a geometric vector model of HSV SAR with a spiral trajectory is established, and the corresponding signal and trajectory characteristics are analyzed. Subsequently, the 3-D coordinate positioning approach based on HSV SAR with spiral trajectory is proposed. The proposed method achieves target positioning via two-dimensional (2-D) multi-view imaging, SAR target matching, and 3-D coordinate calculation. This method is relatively novel and provides superior imaging and positioning performance. Simulation, real data, and semi-real data experiments are given to validate the effectiveness of the proposed method.

Maneuver provides flexibility for highly squinted synthetic aperture radar (SAR) and also means complicated signal characteristics in the echo, especially for ground moving target imaging (GMTIm). This article analyzes the interaction of parameters between the maneuvering platform and the moving target. The analysis suggests that three key factors should be taken into account: azimuth spectrum aliasing, Doppler centroid ambiguity, and high-order errors. To deal with these challenges, a novel GMTIm methodology for maneuvering platform is presented. The proposed approach employs an advanced parameter estimation method based on the extended generalized high-order ambiguity function (EGHAF), which enables simultaneous estimation of high-order phase coefficients across all orders in low signal-to-noise ratio (SNR) conditions through 2-D extension. Due to the estimation and compensation for higher order phases, which are usually ignored in conventional methods, the proposed method is more suitable for moving target imaging with maneuvering platforms. The superiority of the proposed approach is verified by simulation and real data results.

The extended reality (XR) is one of the most widely used approaches for accessing the metaverse world. The metaverse and XR aim to blend the virtual and real parts, offering an immersive and interactive experience. Diminished reality (DR) is a subset of XR that specifically addresses the real-time occlusion, removal, and transparency of objects in the environment. As an immersive technology, DR has been utilized in academia and industry to tackle a wide range of engineering problems. However, there is a little investigative work about DR technique evaluations. In this survey, we categorize the state-of-the-art research into two major categories and six subcategories, providing a novel perspective. We further analyze and evaluate the application effects and performance of these approaches from both quantitative and qualitative perspectives, considering the technical performance and user experience of DR techniques. Finally, we provide an overview of potential future directions for DR applications.

Land surface temperature (Ts) is one of the important parameters of the earth’s surface, but the temporal and spatial incompleteness of Ts data has severely limited its application in many important fields, such as climate change, extreme weather, numerical weather prediction, and agro-meteorological disasters. The MODIS daily Ts data is a relatively high temporal and spatial resolution data, but it also has a large amount of missing data due to the influence of clouds or other atmospheric conditions. Gap filling is currently the only means of obtaining complete high spatial and temporal resolution remote sensing Ts data. However, the current gap filling results either fail to guarantee the filling accuracy due to the difficulty of obtaining ground observation data, or fail to generalize the gap filling method over a large area due to its inefficiency. In this study, we firstly obtained the daily mean Ts (dmTs) data under MODIS clear-sky condition using multiple linear regression based on Ts data from nearly 2400 meteorological observation stations, and then proposed a new method to fill Ts in the cloudy-sky condition. The validation with in situ data showed that the precision of filled Ts in clear-sky condition indicates its root mean squared error (RMSE) is between 1.52 K to 2.36 K, and in cloud-sky condition its RMSE is 2.73 K. It is confirmed that the method has the advantages of efficiency, simplicity and accuracy and is the most suitable method for filling Ts data at national and continental levels. The all-weather 1-km dmTs data reconstructed in this study is of great value on urban heat island intensity studying, air temperature generating, drought monitoring, and other associated applications.

Augmented reality (AR) is a burgeoning interaction technology with the ability to provide users with immersive everyday experiences. Shopping is among the most common experiences. When consumers purchase products, they often encounter difficulties in obtaining detailed information relevant to their interests, such as ingredients, origin, and product comparisons, leading to frustration. This situation can be undeniably frustrating. We can use visualization technology [18] to organize it more orderly and friendly. This paper proposes a mobile augmented reality-based application framework, Product Magic Mirror (PMM), which helps to integrate basic visualization design into the application. The augmented information can be rich, e.g. they can be some visualizations and vivid data videos. In the evaluation, we simulated a goods purchase scene, and applied information to the real world by using AR. The real environment and virtual objects were superimposed on the same screen in real time, so as to achieve an experience beyond reality [24]. In our user survey, users rated our tools as a way to make better shopping choices, giving them a positive score for an immersive shopping experience. We predict that displaying information such as instructions, ingredients, and/or user review information next to products could allow consumers to make better consumption choices while reducing decision time and making shopping more immersive.

We propose eBoF, a novel time-varying ensemble data visualization approach based on the Bag-of-Features (BoF) model. In the eBoF model, we extract a simple and monotone interval from all target variables of ensemble scalar data as a local feature patch. Each local feature of a semantically simple single interval can be defined as a feature patch within the BoF model, with the duration of each interval (i.e., feature patch) serving as its frequency. Feature clusters in ensemble runs are then identified based on the similarity of temporal correlations. eBoF generates clusters along with their probability distributions across all feature patches while preserving the geo-spatial information, which is often lost in traditional topic modeling or clustering algorithms. The probability distribution across different clusters can help to generate reasonable clustering results, evaluated by domain knowledge. We conduct case studies and performance tests to evaluate the eBoF model and gather feedback from domain experts to further refine it. Evaluation results suggest the proposed eBoF can provide insightful and comprehensive evidence on ensemble simulation data analysis.