Search Results (83)

While the wavenumber-domain approach enables large-angle inverse synthetic aperture radar (ISAR) cross-range scaling, its practical application remains constrained by the target’s non-uniform rotation and scene center offset (SCO). In response to this issue, this paper introduces a novel large-angle ISAR cross-range scaling method through a joint estimation method based on the wavenumber domain. A non-uniform rotational wavenumber-domain signal model with SCO is developed. Utilizing this model and the sensitivity of wavenumber-domain imaging to SCO, a joint estimation algorithm that combines particle swarm optimization (PSO) and image entropy evaluation is proposed, achieving accurate parameter estimation. Leveraging the estimated parameters, the range and cross-range scaling factors in the wavenumber-domain imaging are derived, facilitating ISAR cross-range scaling with higher accuracy than the traditional method. The effectiveness and robustness of the proposed method are validated under various conditions, through scattering point and electromagnetic computing simulation. Full article

The high-precision estimation of multi-dimensional parameters for spatial targets based on high-resolution range profiles is crucial for target recognition. However, existing estimation methods face difficulties in resolving the strong coupling between the target shape and the micro-motion parameters, as well as in fully utilizing micro-motion information under complex modulation characteristics. To address these challenges, this paper proposes a multi-dimensional parameter-estimation method for spatial targets based on micro-range decomposition. A micro-range model of the target is first constructed, and the micro-range modulation characteristics are analyzed. Then, micro-range coefficients are selected based on their Cramér–Rao lower bound (CRLB), and the correlation between these coefficients and target parameters is exploited for scattering center matching. An optimization model is further built for multi-dimensional parameter estimation, enabling the accurate estimation of parameters such as precession frequency, precession angle, and structural dimensions under both single-view and multi-view conditions. The experimental results show that in the dual-view case, all parameters are estimated with relative errors (REs) below 1.15% and root mean square error (RMSE) values below 0.05. In the single-view case, key parameters are estimated with REs under 15%. Compared with conventional methods, the proposed method achieves lower RMSE and significantly improved robustness and stability. These results demonstrate the effectiveness and practical potential of the proposed method for spatial target parameter estimation. Full article

Inverse Synthetic Aperture Radar (ISAR) images are extensively used in Radar Automatic Target Recognition (RATR) for non-cooperative targets. However, acquiring training samples for all target categories is challenging. Recognizing target classes without training samples is called Zero-Shot Learning (ZSL). When ZSL involves multiple modalities, it becomes Multi-modal Zero-Shot Learning (MZSL). To achieve MZSL, a framework is proposed for generating ISAR images with optical image aiding. The process begins by extracting edges from optical images to capture the structure of ship targets. These extracted edges are used to estimate the potential locations of the target’s scattering centers. Using the Geometric Theory of Diffraction (GTD)-based scattering center model, the edges’ ISAR images are generated from the scattering centers. Next, a mapping is established between the edges’ ISAR images and the actual ISAR images. Neighbor-Adapted Local Linear Embedding (NALLE) generates pseudo-ISAR images for the unseen classes by combining the edges’ ISAR images with the actual ISAR images from the seen classes. Finally, these pseudo-ISAR images serve as training samples, enabling the recognition of test samples. In contrast to the network-based approaches, this method requires only a limited number of training samples. Experiments based on simulated and measured data validate the effectiveness. Full article

To investigate the effectiveness of water environment governance in China, this study employs the Super-SBM model to measure the WEGE (water environment governance efficiency) of 283 prefecture-level cities in China from 2013 to 2022. Multidimensional decomposition is conducted using the Dagum Gini coefficient, kernel density estimation, convergence models, and the Tobit model. The findings reveal the following: (1) China’s WEGE is generally at a low-efficiency development stage, exhibiting a pattern of “western regions > central regions > eastern regions”. WEGE evolves from “scattered distribution” to “multi-center aggregation”. (2) The overall Gini coefficient for WEGE in China is relatively low, with an average of 0.120. Intra-group differences and transvariation intensity are the primary sources of regional disparities. (3) The country and the three major regions exhibit right-tailed and multi-polar phenomena. (4) σ-convergence is observed exclusively in the eastern area, whereas both absolute and conditional β-convergence are evident throughout the country as well as within the three major regional divisions. (5) Government intervention has a significant positive impact on WEGE, while artificial intelligence, spatial agglomeration, and industrial structure upgrading exert negative effects on WEGE. Therefore, it is urgent to pay attention to the regional differences in WEGE and implement practical measures for collaborative water environment governance. Full article

In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics make envelope alignment and phase compensation difficult, thereby limiting integration gain. To address these issues, in this study, we conducted an in-depth analysis of the echo model of cylindrical space targets (CSTs) based on different types of scattering centers. Building on this foundation, the multi-scale subaperture coherent integration Transformer (MsSCIFormer) was proposed, which integrates MsSCI with a Transformer architecture to achieve precise detection and motion parameter estimation of space targets in low-SNR environments. The core of the method lies in the introduction of a convolutional neural network (CNN) feature extractor and a dual-attention mechanism, covering both intra-subaperture attention (Intra-SA) and inter-subaperture attention (Inter-SA). This design efficiently captures the spatial distribution and motion patterns of the scattering centers of space targets. By aggregating multi-scale features, MsSCIFormer significantly enhances the detection performance and improves the accuracy of motion parameter estimation. Simulation experiments demonstrated that MsSCIFormer outperforms traditional moving target detection (MTD) methods and other deep learning-based algorithms in both detection and estimation tasks. Furthermore, each module proposed in this study was proven to contribute positively to the overall performance of the network. Full article

In the context of 3D geometric reconstruction from sequential inverse synthetic aperture radar (ISAR) images, the accurate scatterer trajectory association is a critical step. Aiming at the above problem, an enhanced scatterer trajectory association method is proposed by designing a modified label Gaussian mixture probability hypothesis density (ML-GM-PHD) filtering algorithm. The algorithm commences by constructing a general motion model for scatterers across sequential ISAR images, followed by an in-depth analysis of their motion characteristics. Subsequently, the actual projected positions and measurements of the scattering centers of the observed target are treated as random finite sets, which allows us to reformulate the scatterer trajectory association into a maximum a posteriori (MAP) estimation problem. After that, a ML-GM-PHD filtering algorithm is proposed to realize the scatterer trajectory association. Furthermore, the proposed method is applied to ISAR images in both the forward and reverse directions, enabling the fusion of trajectories from opposing directions to bolster the completeness of the scatterer trajectories. Finally, the factorization method is performed on the scatterer trajectory matrix to implement the 3D geometry reconstruction of the scattering centers in the observed target. Experimental results based on random points and electromagnetic data verify the effectiveness and performance priority of the proposed algorithm. Full article

A new estimation strategy on locations of two-dimensional target scattering centers for radar target recognition is developed by using multiple high-resolution range profiles (HRRPs). Based on the range information contained in multiple HRRPs obtained from various observation angles, the estimated target scattering centers can be successfully located at the intersection points of the lines passing through the multiple HRRP points. This geometry-based algorithm can significantly reduce the computational complexity while ensuring the ability to estimate the two-dimensional target scattering centers. The computational complexity is formulated and compared to that of the conventional methods based on the synthetic aperture radar (SAR) images and HRRP sequences. In order to verify the performance of the proposed algorithm, the numerical and experimental results for three different types of aircraft were compared to those from SAR images. At the end of this article, the estimated radar scattering centers are used as the target features for the conventional classifier machine to confirm its target classification performance. Full article

Buddhist culture in Liaoning has a long and rich history. The continuous spread of Buddhism has promoted the development of Buddhist architecture, leaving us a rich architectural art heritage. Furthermore, it has also profoundly influenced China’s architectural characteristics, social culture, and economic development. This paper takes Buddhist temples and pagodas in Liaoning as the research objects and uses methods such as the geographic concentration index, nearest neighbor index, kernel density estimation, and standard deviation ellipse to analyze their spatio-temporal distribution characteristics and influencing factors across different periods. 1. Temporal distribution. During the Liao Dynasty (907–1125 AD) and the Qing Dynasty (1636–1912 AD), the construction of Buddhist temples and pagodas was the highest, with a linear increase in the Qing Dynasty. 2. The overall spatial distribution of Buddhist temples and pagodas in Liaoning is uneven, showing an agglomeration distribution state. The distribution status of different periods was different, and the Ming (1368–1644 AD) and Qing dynasties (1636–1912 AD) showed obvious aggregation distribution. The overall state is “more in the west and less in the east” and “more in the north and less in the south”. 3. In different periods, the spatial distribution direction of Buddhist temples and pagodas in Liaoning was relatively obvious and was southwest–northeast, and the center of gravity gradually shifted to the northwest. 4. The kernel density of different periods presents the density distribution and area of each period. The overall distribution is dense to scattered and then to highly dense. 5. The spatio-temporal distribution characteristics of Buddhist temples and pagodas in Liaoning are mainly composed of deep-seated political factors, rapid economic development and stable social environment, diverse culture, natural geography, cultural relics protection, and the artistic value of Buddhist architecture in the Liaoning region. Full article

A predictive model utilizing near-infrared spectroscopy was developed to estimate the loss on drying, total contents of crocin I and crocin II, and picrocrocin content of saffron. Initially, the LD values were determined using a moisture-ash analyzer, while HPLC was employed for measuring the total contents of crocin I, crocin II, and picrocrocin. The near-infrared spectra of 928 saffron samples were collected and preprocessed using first derivative, standard normal variable transformation, detrended correction, multivariate scattering correction, Savitzky–Golay smoothing, and mean centering methods. Leveraging the partial least squares method, regression models were constructed, with parameters optimized through a selective combination of the above six preprocessing methods. Subsequently, prediction models for loss on drying, total contents of crocin I and crocin II, and picrocrocin content were established, and the prediction accuracy of the models was verified. The correlation coefficients and root mean square error of loss on drying, total contents of crocin I and crocin II, and picrocrocin content demonstrated high accuracy, with R² values of 0.8627, 0.8851, and 0.8592 and root mean square error values of 0.0260, 0.0682, and 0.0465. This near-infrared prediction model established in the present study offers a precise and efficient means of assessing loss on drying, total contents of crocin I and crocin II, and picrocrocin content in saffron and is useful for the development of a rapid quality evaluation system. Full article

The domain wall energy is calculated by the balance between exchange, magnetocrystalline anisotropy and magnetoelastic energy contributions. The described method is theoretical and is based on experimental measurements of neutron inelastic scattering. The domain wall energy is determined by both finding the minimum of energy and deriving the energy and setting it to zero. The determination was undertaken for the discrete case, and this means that the calculation was performed for each plane or atomic layer. This is in contrast with the Bloch wall, which assumes continuum mean. The energy of the Lilley domain wall was discussed. Most of the energy of the Bloch wall was comprised inside the Lilley distance (above 99.9% of the energy). Antiferromagnetic interactions strongly decreased the domain wall energy. The negative terms due to antiferromagnetism must be considered in the Hamiltonian describing the exchange energy terms. The domain wall energy and width of terbium were reassessed. The values varied between 83.7 and 95.2 Kelvin (10.3 to 11.2 ergs/cm²). The domain width was estimated to be 57 Angstroms. It was found that a significant part of the total domain wall energy was concentrated on the planes at the center of the domain wall. Full article

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data. Full article

We performed continuous long-term measurements of PM_2.5 mass, comprehensive chemical composition, and optical properties, including scattering and absorption coefficients, from March 2011 to December 2020 at the Metropolitan Air Quality Research Center in Seoul, South Korea. PM_2.5 peaked at 38 μg/m³ in 2013 and has been declining steadily since then, reaching 22 μg/m³ in 2020. The extinction coefficients also decreased with the decline in PM_2.5, but the correlation between the two factors was not as pronounced. This deviation was mainly attributed to the rapid changes in the chemical composition of PM_2.5 over the same period. The mass contribution of sulphate to PM_2.5 decreased from 33.9 to 24.1%, but the fraction of nitrate and organic carbon increased from 23.4 and 20.0 to 34.1 and 32.2%, respectively, indicating that sulphate has been replaced by nitrate and organic carbon over the past decade. To assess the effect of changing aerosol chemical compositions on light extinction, we compared the measured extinction coefficients with those estimated via the various existing light extinction approaches, including the revised IMPROVE algorithm. We found that the simplified linear regression model provided the best fit to our data, with a slope of 1.03 and R² of 0.87, and that all non-linear methods, such as the IMPROVE algorithms, overestimated the observed long-term light extinction by 23 to 48%. This suggests that the simple linear regression scheme may be more appropriate for reflecting the varying aerosol conditions over long periods of time, especially for urban air. However, for conditions where the chemical composition does not change much, non-linear methods such as the IMPROVE scheme are likely to be more appropriate for reproducing light extinction. Full article

Extended target tracking (ETT) based on random matrices typically assumes that the measurement model is linear. However, nonlinear measurements (such as range and azimuth) depending on locations of a series of unknown scattering centers always exist in many practical tracking applications. To address this issue, this paper proposes an iteratively extended target tracking based on random matrices by using decorrelated unbiased conversion of nonlinear measurements (ETT-IDUCM). First, we utilize a decorrelated unbiased converted measurement (DUCM) method to convert nonlinear measurements depending on unknown scatters of target extent in polar coordinates into the ones in Cartesian coordinates with equivalent measurement noise covariances. Subsequently, a novel method, combining iteratively extended Kalman filter (IEKF) updates with variational Bayesian (VB) cycles is developed for precise estimation of the target’s kinematic state and extension. This method leverages the synergy between external IEKF iterations, which use the estimated state as a new prediction and input for DUCM, and internal VB iterations, which realize a closed-form approximation of the joint posterior probability. This approach progressively enhances estimation accuracy. Simulation results demonstrate the ETT-IDUCM algorithm’s superior precision in estimating the target’s kinematic state and extension compared to existing methods. Full article

The strong wind-induced current and sea level have influences on the wave distribution in a tropical cyclone (TC). In particular, the wave–current interaction is significant in the period in which the TC passed the mesoscale eddy. In this study, the wave fields of Typhoon Chan-hom (2015) are hindcastly simulated using a coupled oceanic model that utilizes a nested triangle grid, i.e., the finite-volume community ocean model-simulating waves nearshore (FVCOM-SWAVE) model. The forcing wind field is composited from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data and the simulation using a parametric Holland model, denoted as H-E. The open boundary fields include tide data from TPOX.5 and the hybrid coordinate ocean model (HYCOM) global datasets, including sea surface temperature (SST), sea surface salinity, sea surface current, and sea level data. The simulated oceanic parameters (e.g., the significant wave height, SWH) are validated against the measurements from the Jason-2 altimeter, yielding a root mean square error (RMSE) of 0.58 m for the SWH, a correlation (COR) coefficient of 0.94, and a scatter index (SI) of 0.23. Similarly, the simulated SSTs are compared with the remote sensing products of the remote sensing system (REMSS) and the measurements from Argos, yielding an RMSE of <0.8 °C, a COR of >0.95, and an SI of <0.04. The significant zonal asymmetry of the wave distribution along the typhoon track is observed. The Stokes drift is calculated from the FVCOM-SWAVE simulation results, and then the contribution of the Stokes transport is estimated using the Ekman–Stokes numbers. It is found that the ratio of the Stokes transport to the total net transport can reach >80% near the typhoon center, and the ratio is reduced to approximately <20% away from the typhoon center, indicating that Stokes transport is an essential aspect in the water mixing during a TC. The mesoscale eddies are detected by the sea level anomalies (SLA) fusion data from AVISO. It is found that the significant wave heights, Stokes drift, and Stokes transport inside the eddy area were higher than those outside the eddy area. These parameters inside the cold mesoscale eddies were higher than t inside the warm mesoscale eddies. Otherwise, the SST mainly increased within the cold mesoscale eddies area, while decreased within the warm mesoscale eddies area. The influence of mesoscale eddies on the SST was in proportion to the eddy radius and eddy EKE. Full article

Forest canopy height estimation is essential for forest management and biomass estimation. In this study, we aimed to evaluate the capacity of TanDEM-X interferometric synthetic aperture radar (InSAR) data to estimate canopy height with the assistance of an external digital terrain model (DTM). A ground-to-volume ratio estimation model was proposed so that the canopy height could be precisely estimated from the random-volume-over-ground (RVoG) model. We also refined the RVoG inversion process with the relationship between the estimated penetration depth (PD) and the phase center height (PCH). The proposed method was tested by TanDEM-X InSAR data acquired over relatively homogenous coniferous forests (Teruel test site) and coniferous as well as broadleaved forests (La Rioja test site) in Spain. Comparing the TanDEM-X-derived height with the LiDAR-derived height at plots of size 50 m × 50 m, the root-mean-square error (RMSE) was 1.71 m (R² = 0.88) in coniferous forests of Teruel and 1.97 m (R² = 0.90) in La Rioja. To demonstrate the advantage of the proposed method, existing methods based on ignoring ground scattering contribution, fixing extinction, and assisting with simulated spaceborne LiDAR data were compared. The impacts of penetration and terrain slope on the RVoG inversion were also evaluated. The results show that when a DTM is available, the proposed method has the optimal performance on forest height estimation. Full article