Abstract. This paper proposes a two-stage framework to address the inverse diffraction grating problem with limited-aperture data. The first stage introduces a deep learning network for data retrieval, featuring a dual-branch, cross-attention architecture. Motivated by an information-theoretic analysis, this design is tailored to separate and adaptively fuse the diffracted field’s low- and high-frequency components, effectively handling their distinct noise sensitivities. The second stage employs a computationally efficient Newton-type algorithm for shape reconstruction, which avoids the need for a forward solver at each iteration. Numerical experiments show that our framework provides accurate and robust reconstructions.

Accurate and rapid localization can improve geographic information system (GIS) tasks to support disaster rescue and resource exploration in field operations. However, the existing localization methods suffer from high costs, low efficiency, and poor accuracy of long-distance targets due to environmental factors like terrain and landforms. Therefore, we propose a multilevel localization framework based on mobile phones for different field scenarios. First, we investigated a rapid localization method constrained by scene contextual features in the field with salient features. Second, we designed a single-point localization method that combines DEM data and mobile phones when high-precision DEM data are available for the region. Third, we studied a map-matching corrected joint localization with mobile phone images in the field lacking salient features and high-precision DEM data. Finally, we developed a prototype system for field localization and selected a forest scene for experimental analysis. The results showed that the proposed mobile phone-based localization framework supports long-distance localization in the field. The localization accuracy in three different field scenarios is within 100 meters, and the localization efficiency reaches a minute level, which can effectively support the convenient, rapid, high-precision, and long-distance localization tasks in field scenes.

An important step in implementing geospatial digital twins is to enhance the expressiveness of virtual geographical scenes for the physical world. However, the existing virtual geographical scenes cannot quickly express the dynamically changing geographic environment for remote users due to the inefficient handling of modeling processes, user perception, and remote sharing. The research analysed the concept and characteristics of geospatial digital twins, and constructed the virtual geographical scene ontology, based on which we developed geographical spatiotemporal semantic rules and designed a dynamic annotation algorithm to enhance the representation of virtual geographical scenes. Finally, we investigated a real-time transmission method of panoramic video based on 5 G and used immersive virtual reality (IVR) to realize the user experience of remote immersion in geographical scenes. We selected a specific geographic environment containing multiple typical geographic entities to develop three prototype systems for experimental analyses. The results showed that the proposed method enabled users to view the virtual geographical scene on a VR device. The average latency for this process was 14.72 seconds. Compared with the virtual geographical scenes constructed by traditional methods, the experiments showed the proposed method advantageous in comprehensiveness, timeliness, and photorealism and abilities to enhance the user’s geographical scene perception.

Public’s rational flood mitigation behaviors depend on accurate perception of flood risks. The use of natural language for flood risk perception is an effective approach, and it is critical to ensure the accuracy and comprehensibility of the flood information provided by the system in natural language dialogues. This study presents a framework for large language model (LLM) that is constrained by flood knowledge and can interact with geographic information system (GIS), aimed at enhancing the public’s perception of flood risks. We tested the performance of LLM within this framework and the results demonstrate that LLM can generate accurate information about floods under the constraints of entities and relationships in the knowledge graph, and interact with GIS to produce personalized knowledge through real-time coding. Furthermore, we conducted flood risk perception experiments on users with different cognitive levels. The results indicate that using natural language dialogue can narrow the differences brought about by cognitive levels, allowing the public to equally access knowledge related to flood events.

As an important application of virtual geographic environments (VGEs), virtual disaster scenes are essential in enhancing the public’s risk awareness. However, existing virtual disaster scene visualization methods lack expert guidance and fail to meet the public’s requirements, resulting in an ineffective public understanding. Therefore, this paper proposes a knowledge-guided disaster scene 3D visualization framework. First, the public’s demand for disaster scene visualization is analyzed, and a geographic knowledge graph of disaster scenes is constructed. Second, through the guidance of the knowledge graph, the virtual disaster scenes are fusion modeled and suitability represented. Third, a diverse organization and adaptive scheduling method of disaster scene data for multi-computing devices is established. Finally, we developed a prototype system for disaster scene visualization, selected a typical disaster, and conducted cognitive experiments with eye-tracking technology. The results show that the proposed method can effectively support the adaptive visualization of virtual disaster scenes for four computing devices and maintain an efficient frame rate. In addition, compared with other disaster scene visualization methods, our framework incorporates semantic knowledge of scene, user, demand, and space. It can effectively convey disaster information and help the public cognize disaster risks and has significant advantages in modeling standardization, personalization, and adaptability.

The azimuth multichannel synthetic aperture radar (SAR) technique can simultaneously achieve high resolution and wide swath, which is important for disaster management, sea and land traffic observation, and environmental monitoring. However, nonuniform sampling leads to multichannel imaging quality degradation such as serious ghost targets or deterioration of signal-to-noise ratio (SNR). In this article, a multichannel SAR imaging algorithm is proposed. The key is to perform spectral selection and extrapolation based on the time-frequency characteristics of multichannel signals under nonuniform sampling conditions to obtain subimages with different ghost distributions. By implementing subimage fusion, the final imaging result can be obtained. Simulations and satellite real data experiments are conducted to verify the effectiveness of the proposed algorithm. The imaging performances of the proposed algorithm in terms of ghost-to-real target ratio (GRTR), and SNR are investigated with respect to the pulse repetition frequency (PRF). Moreover, simulation results also demonstrate that the proposed algorithm exhibits a more robust and consistent overall performance than existing methods.

Due to the sub-Nyquist sampling, compressive sensing (CS) theory can relieve the contradiction between high-resolution and wide-swath in the field of microwave imaging and it has attracted extensive attention. However, conventional CS-based imaging models always require sparse properties of the unrecovered scene. This paper proposes a synthetic aperture radar (SAR) change imaging in the transforming domain based on CS algorithms, which converts the recovery of the observed scene to that of scene change between the historical observation and the current observation. Firstly, in the sparse transforming domain constructed by historical observation, a new complex-data sparse microwave imaging model is built by the amplitude-phase separated operation. And then a block coordinate descent algorithm is used to recover the change with sub-Nyquist sampling echoes. At last, the scene of the current observation can be achieved by integrating the recovered change with the historical observation. The effectiveness of change imaging in the transforming domain is verified on both simulated and real SAR images.

The minimum-energy-criterion (MEC) algorithm introduces the correlation between frequency points to the reconstruction process and uses the optimization method to realize a good reconstruction of multichannel signals in the nonuniform sampling scenario. However, the reconstruction performance of the MEC algorithm will change with pulse repetition frequency (PRF). In this paper, we proposed an improved minimum-energy-criterion (IMEC) algorithm that sets the appropriate reconstruction bandwidth according to the system requirements, which achieves a more robust reconstruction performance than the MEC algorithm.

The reconstruction of multipolar acoustic or electromagnetic sources from their far-field signature plays a crucial role in numerous applications. Most of the existing techniques require dense multi-frequency data at the Nyquist sampling rate. The availability of a sub-sampled grid contributes to the null space of the inverse source-to-data operator, which causes significant imaging artifacts. For this purpose, additional knowledge about the source or regularization is required. In this letter, we propose a novel two-stage strategy for multipolar source reconstruction from sub-sampled sparse data that takes advantage of the sparsity of the sources in the physical domain. The data at the Nyquist sampling rate isrecoveredfrom sub-sampled data and then a conventional inversion algorithm is used to reconstruct sources. The data recovery problem is linked to a spectrum recovery problem for the signal with thefinite rate of innovations(FIR) that is solved using anannihilating filter-based structured Hankel matrix completion approach(ALOHA). For an accurate reconstruction, a Fourier inversion algorithm is used. The suitability of the approach is supported by experiments.

Abstract—Auto-registration video synthetic aperture radar (ViSAR), which requires real time pixel index unifying and resolution matching, is of great significance due to its applicability in multi-aspect observation and continuous monitoring. The phase error induced by the wavefront planar assumption, however, varies with different ViSAR frames, which limits the size of auto-registration imaging scene. To enlarge the auto-registration imaging swath, a parameter-adjusting auto-registration overlapped subaperture algorithm (PAAR-OSA) is proposed in this paper. By collaboratively designing the subapertures within each frame and among different frames in the stabilized-scene coordinate and cooperatively compensating the phase error of all frames, auto-registration with larger imaging swath can be achieved. Both the point targets and distributed targets validation results verify the superiority of the proposed method compared with existing algorithms.

Bistatic geosynchronous synthetic aperture radar (GEO SAR) system with a stationary receiver is capable of continuous observation and frequent revisit over certain area, which can be applied in disaster monitoring. The mismatch between the true height and the assumed height of the imaging plane may result in azimuth defocusing. This paper exploit the azimuth defocusing effect to estimate the height of strong scatterers. By estimating and compensating the quadratic phase error, fine imaging results in the reference plane are obtained. The height can then be retrieved accurately combining the phase error and the imaging position in the imaging plane. Simulation based on point targets verifies the proposed method.

This paper applies the overlapped subaperture algorithm (OSA) for geosynchronous (GEO) spaceborne/missile-borne bistatic SAR (GEO SMB-BISAR) imaging. Compared with the original bistatic SAR, GEO SMB-BISAR performs better in flexibility and security. The essence of the OSA is to perform the imaging process twice by azimuth subaperture division. The quadratic phase errors (QPE) induced by the planar wavefront assumption can be compensated by two steps imaging processing within and between subapertures. The OSA can expand a wider imaging swath than the polar format algorithm (PFA). Validation results demonstrate the validity of the OSA methodology.

Multistatic geosynchronous synthetic aperture radar (GEO SAR) system, with a geosynchronous illuminator and two stationary receivers, can provide high-resolution images for certain area. The spatially variant gaps in the azimuth spectrum, however, may introduce artifacts into images, which degrade the image quality. To fill the spectrum gaps, a new imaging method using spectrum alignment and extrapolation is proposed in this letter. The spectrum of the scene is aligned based on spectrum shifting and range Doppler projection, and a complete azimuth spectrum is obtained by spectrum extrapolation and coherent summation. Simulation results of point targets and extended targets verify the efficacy of the proposed method.

Due to the complexity and inaccuracy of the force model of low earth orbit(LEO) satellite, the ephemeris data is not accurate enough for translation compensation, which will cause the residual translation component error, then leading to the dissatisfaction of the range and azimuth resolution for imaging. To face the problem above, the secondary correction of translational component is carried out by updating the ephemeris data fitted by the ephemeris data calculated by orbit two lines elements(TLE) and center slant which is estimated with the real echo data of the space station through correlation method, after a coarse correction-the registration of ephemeris data and echo data-and then the Inverse synthetic aperture radar(ISAR) image of the space station is obtained by combining the polar format algorithm(PFA), providing the foundation for monitoring, identification and tracking of space targets. The imaging results prove the validity of the method, which can be referred to in the imaging of LEO satellite and the real data processing based on ephemeris data.

Quasi geostationary orbit (GSO) synthetic aperture radar (SAR) has the superiority of continuous wide view Earth observation. High resolution is achieved at the cost of long integration time. In this paper we propose a new imaging method for quasi GSO SAR constellation. By implementing spectrum gap filling, the azimuth resolution corresponding to the gapped synthetic aperture covered by the constellation is reached. Simulation results validate the proposed method.

Geostationary orbit (GSO) synthetic aperture radar (SAR) constellation has the advantage of continuous Earth observation and can achieve high azimuth resolution. In this paper we propose an imaging method for GSO SAR constellation. By implementing data fusion, the azimuth resolution corresponding to the synthetic aperture covered by the constellation is achieved. Simulation results verify the effectiveness of the proposed method.

The airborne or spaceborne early warning radar faces great challenges of detecting and tracking weak moving targets with signal fluctuations. An adaptive detector is proposed to improve the detection performance by jointly processing the space-time data sets received by multistatic multiple-input multiple-output (MIMO) radar, and fusing the measurement vectors information. Monte-Carlo simulations demonstrate that the proposed detector can implement early detection of weak moving targets with the target detection probability 0.5 and the false alarm probability 10-1under the condition of 5dB signal-to-noise ratio.

One of the most important properties in geosynchronous synthetic aperture radar (GEO-SAR) is the resolution in the ground range plane, whose relationship with resolution in the slant range plane is complicated, which results in evaluation difficulties. This paper proposes a method of evaluation of the ground resolution using geometric correction, while transforming the GEO-SAR images from the slant range plane to the ground range plane. Simulation results verify the effectiveness of the proposed method.