Search Results (59)

The McMurdo Dry Valleys (DVs) of South Victoria Land, Antarctica, constitute the largest ice-free region on the continent and one of Earth’s most Mars-analog environments. Their hyper-arid polar desert conditions offer a unique setting for investigating surface weathering and mineralogical processes under extreme climates. This study presents the first regional-scale mapping of alteration and crystalline weathering minerals across the McMurdo DVs. It uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data; visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands were analyzed through a Spectral Hourglass Workflow, endmember extraction, and spectral unmixing with Matched Filtering (MF) and Constrained Energy Minimization (CEM). Inter-algorithm consistency analysis between MF and CEM yielded 78.83% overall agreement with a Kappa coefficient of 0.75, indicating strong methodological consistency in mineral discrimination using ASTER VNIR+SWIR data. It should be noted that this agreement reflects internal algorithmic robustness rather than independent geological validation. Geological reliability is instead supported by documented field observations, lithological map comparisons, and spectral correspondence with the USGS spectral library. Validation employed documented field observations, lithological maps, and the USGS spectral library. Results reveal distinct spatial distributions of hematite-limonite/goethite, jarosite, kaolinite/smectite-illite-pyrophyllite-alunite, muscovite, hydrous silica/sericite/jarosite/hematite, epidote/chlorite, and calcite, closely associated with lithological units and unconsolidated deposits in Taylor, Wright, Victoria, and McKelvey Valleys. An inter-algorithm consistency check achieved 78.83% overall accuracy with a Kappa coefficient of 0.75, underscoring the robustness of ASTER VNIR+SWIR data for Antarctic mineral discrimination despite localized spectral mixing. Beyond refining the geological understanding of the McMurdo DVs, these results establish ASTER as an effective tool for regional mineralogical mapping in inaccessible polar terrains. The findings further strengthen the role of the Dry Valleys as a terrestrial analog for Mars, where similar mineralogical assemblages and spectral ambiguities have been observed, thereby contributing to both Antarctic geoscience and planetary exploration frameworks. Full article

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH₄ concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH₄ (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission. Full article

The rapid evolution of communication systems, exemplified by the Internet of Things (IoT), demands increasingly stringent reliability in both communication and sensing. While Orthogonal Frequency Division Multiplexing (OFDM) struggles to meet the challenges posed by complex scenarios, Orthogonal Chirp Division Multiplexing (OCDM) has gained attention for its robustness and spectral efficiency in Integrated Sensing and Communication (ISAC) systems. However, its sensing mechanism remains insufficiently explored. This paper presents a theoretical analysis of the communication and sensing performance of OCDM waveforms within the ISAC framework. Specifically, a closed-form BER expression under equalization is derived, alongside the ambiguity function and detection performance evaluation under matched filter (MF) and Generalized Likelihood Ratio Test (GLRT) detectors with a constant false alarm rate (CFAR) criterion. Simulation results demonstrate that OCDM offers comparable sensing performance to OFDM while achieving superior communication robustness in complex environments. Full article

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

This work presents a comprehensive technological study of dielectric resonator-based microstrip filters (DRMFs), encompassing the design, fabrication, and rigorous characterization of the $T{E}_{01\delta }$ mode. Through systematic coupling analysis, we demonstrate filters featuring novel input–output coupling techniques and innovative implementations of both transmission zeros (4-2-0 configuration) and equalization zeros (4-0-2 configuration), specifically designed for demanding space and radar receiver applications, while the loaded quality factor ($Q_L$) and insertion loss do not match those of dielectric resonator cavity filters (DRCFs), our solution significantly surpasses conventional microstrip filters (MFs), achieving $Q_L$> 3000 compared to typical $Q_L$≈ 200 for coupled-line MFs in X-band. The fabricated filters exhibit exceptional performance as follows: input reflection ($S_{11}$) below −18 dB (4-2-0) and −16.5 dB (4-0-2), flat transmission response ($S_{21}$), and out-of-band rejection exceeding −30 dB. Mechanical tuning enables precise control of input–output coupling, inter-resonator coupling, cross-coupling, and frequency synthesis, while equalization zeros provide tailored group delay characteristics. This study positions DRMFs as a viable intermediate technology for high-performance RF systems, bridging the gap between conventional solutions. Full article

The clever eye (CE) algorithm has been introduced for target detection in remote sensing image processing. It originally proposes the concept of data origin and can achieve the lowest average output energy compared to both the classical constrained energy minimization (CEM) and matched filter (MF) methods. In addition, it has been theoretically proven that the solutions of the best data origins can be attributed to solving a linear equation, which makes it computationally efficient. However, CE is only designed for single-target detection cases, while multiple-target detection is more demanding in real applications. In this paper, by naturally extending CE to a multiple-target case, we propose a unified algorithm termed multi-target clever eye (MTCE). The theoretical results in CE prompt us to consider an interesting question: do the MTCE solutions also share a similar structure to those of CE? Aiming to answer this question, we investigate a class of unconstrained non-convex optimization problems, where both the CE and MTCE models serve as special cases, which, interestingly, can also be utilized to solve a more generalized linear system. In addition, we further prove that all these solutions are globally optimal. In this sense, the analytical solutions of this generalized model can be deduced. Therefore, a unified framework is provided to deal with such a non-convex optimization problem, where both the solutions of MTCE and CE can be succinctly derived. Furthermore, its computational complexity is of the same magnitude as that of the other multiple-target-based methods. Experiments on both simulations and real hyperspectral remote sensing data verify our theoretical conclusions, and the comparison of quantitative metrics also demonstrates the advantage of our proposed MTCE method in multiple-target detection. Full article

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies. Full article

In recent years, synthetic aperture radar (SAR) technology has been increasingly explored for automotive applications. However, automotive SAR images generated via matched filter (MF) often exhibit challenges such as noisy backgrounds, sidelobe artifacts, and limited resolution. Sparse regularization methods have the potential to enhance image quality. Nevertheless, conventional unweighted ${\mathcal{l}}_1$ regularization methods struggle to address cases with radar cross section (RCS) distributed over a wide dynamic range, often resulting in insufficient sidelobe suppression, amplitude distortion, and inconsistent super-resolution performance. In this paper, we propose a novel reweighted regularization method, termed multi-segment-reweighted regularization (MSR), for automotive SAR image restoration. By introducing a novel weighting scheme, MSR localizes the global scattering point enhancement problem to the mainlobe scale, effectively mitigating sidelobe interference. This localization ensures consistent enhancement capability independent of RCS variations. Furthermore, MSR employs multi-segment regularization to constrain amplitude within the mainlobes, preserving the characteristics of the original response. Correspondingly, a new thresholding function, named Thinner Response Undistorted THresholding (TRUTH), is introduced. An iterative algorithm for enhancing automotive SAR images using MSR is also presented. Real data experiments validate the feasibility and effectiveness of the proposed method. Full article

Methane is a potent greenhouse gas that significantly contributes to global warming, making the accurate quantification of methane emissions essential for climate change mitigation. The traditional matched filter (MF) algorithm, commonly used to derive methane enhancement from hyperspectral satellite data, is limited by its tendency to underestimate methane plumes, especially at higher concentrations. To address this limitation, we proposed a novel approach—the multi-level matched filter (MLMF)—which incorporates unit absorption spectra matching using a radiance look-up table (LUT) and applies piecewise regressions for concentrations above specific thresholds. This methodology offers a more precise distinction between background and plume pixels, reducing noise interference and mitigating the underestimation of high-concentration emissions. The effectiveness of the MLMF was validated through a series of tests, including simulated data tests and controlled release experiments using satellite observations. These validations demonstrated significant improvements in accuracy: In radiance residual tests, relative errors at high concentrations were reduced from up to −30% to within ±5%, and regression slopes improved from 0.89 to 1.00. In simulated data, the MLMF reduced root mean square error (RMSE) from 1563.63 ppm·m to 337.09 ppm·m, and R² values improved from 0.91 to 0.98 for Gaussian plumes. In controlled release experiments, the MLMF significantly enhanced emission rate estimation, improving $R^2$ from 0.71 to 0.96 and reducing RMSE from 92.32 kg/h to 16.10 kg/h. By improving the accuracy of methane detection and emission quantification, the MLMF presents a significant advancement in methane monitoring technologies. The MLMF’s superior accuracy in detecting high-concentration methane plumes enables better identification and quantification of major emission sources. Its compatibility with other techniques and its potential for integration into real-time operational monitoring systems further extend its applicability in supporting evidence-based climate policy development and mitigation strategies. Full article

Methane (CH₄) emissions in coal-energy-rich regions are characterized by hidden emission point sources and highly variable emission rates. While the Matched Filter (MF) method for detecting the CH₄ point source using hyperspectral satellite sensors has been validated for high-emission concentrations, the accurate inversion of low-concentration emissions in complex environments remains challenging. In this study, an ‘end-to-end’ experiment—from emission simulations to satellite spectra and inversion results—has been designed to quantify the impact of internal payload parameters and environmental parameters for CH₄ emission inversions, and perform real-scenario calculations. The study reveals several key findings: (1) Under ideal conditions, 15% of satellite spectral noise contributes to a 13% bias in CH4 detection inversion, and a spectral resolution of 10–14 nm allows the detection of CH₄ emissions with concentrations as low as 350 ppb, above the background level of 1900 ppb. (2) For near-surface aerosols at 2100 nm, an aerosol optical depth (AOD) of 0.1 leads to a low bias of −51.6% with water-soluble aerosols and a strong bias of −69.2% with black carbon aerosols, while dust aerosols induce a medium bias of up to −60.7%. (3) The height of the aerosol layer affects the accuracy of methane inversion, which is up to 7.3% higher under aerosol conditions at 3 km than under aerosol conditions near the ground. (4) When the CH₄ emission source and its diffuse plume are located above a high-reflectance (bright) surface, while the background CH₄ concentration is associated with a low-reflectance (dark) surface, the significant reflectance contrast between the two surfaces leads to a rapid degradation in inversion accuracy. This contrast makes it impossible to effectively extract CH₄ signals when the reflectance difference reaches 0.2. (5) Under harsh conditions, where multiple parameters are present (AOD = 0.2, albedo = 0.2, aerosol layer height (ALH) = 2), the MF method is still able to detect CH₄ emissions, but with a significant error of 74.65%. (6) External environmental variables, particularly atmospheric pressure and water vapor content, significantly influence the inversion accuracy of methane (CH₄) concentrations. Variations in atmospheric pressure induce deviations in the CH₄ concentration distribution, resulting in an average inversion error of −12.06%. Similarly, elevated water vapor levels can lead to a maximum error of −16.2%. These findings highlight the substantial challenges in accurately detecting low-concentration CH₄ emissions. The results offer critical insights for refining CH₄ detection algorithms and enhancing the precision of satellite-based inversions for low-concentration CH₄ point-source emissions. Full article

Sparse synthetic aperture radar (SAR) imaging has demonstrated excellent potential in image quality improvement and data compression. However, conventional observation matrix-based methods suffer from high computational overhead, which is hard to use for real data processing. The approximated observation sparse SAR imaging method relieves the computation pressure, but it needs to manually set the parameters to solve the optimization problem. Thus, several deep learning (DL) SAR imaging methods have been used for scene recovery, but many of them employ dual-path networks. To better leverage the complex-valued characteristics of echo data, in this paper, we present a novel complex-valued convolutional neural network (CNN)-based approximated observation sparse SAR imaging method, which is a single-path DL network. Firstly, we present the approximated observation-based model via the chirp-scaling algorithm (CSA). Next, we map the process of the iterative soft thresholding (IST) algorithm into the deep network form, and design the symmetric complex-valued CNN block to achieve the sparse recovery of large-scale scenes. In comparison to matched filtering (MF), the approximated observation sparse imaging method, and the existing DL SAR imaging methods, our complex-valued network model shows excellent performance in image quality improvement especially when the used data are down-sampled. Full article

Next-generation communication systems demand the integration of sensing, communication, and power transfer (PT) capabilities, requiring high spectral efficiency, energy efficiency, and low cost while also necessitating robustness in high-speed scenarios. Integrated sensing and communication systems (ISACSs) exhibit the ability to simultaneously perform communication and sensing tasks using a single RF signal, while simultaneous wireless information and power transfer (SWIPT) systems can handle simultaneous information and energy transmission, and orthogonal time frequency space (OTFS) signals are adept at handling high Doppler scenarios. Combining the advantages of these three technologies, a novel cyclic prefix (CP) OTFS-based integrated simultaneous wireless sensing, communication, and power transfer system (ISWSCPTS) framework is proposed in this work. Within the ISWSCPTS, the CP-OTFS matched filter (MF)-based target detection and parameter estimation (MF-TDaPE) algorithm is proposed to endow the system with sensing capabilities. To enhance the system’s sensing capability, a waveform design algorithm based on CP-OTFS ambiguity function shaping (AFS) is proposed, which is solved by an iterative method. Furthermore, to maximize the system’s sensing performance under communication and PT quality of service (QoS) constraints, a semidefinite relaxation (SDR) beamforming design (SDR-BD) algorithm is proposed, which is solved using through the SDR technique. The simulation results demonstrate that the ISWSCPTS exhibits stronger parameter estimation performance in high-speed scenarios compared to orthogonal frequency division multiplexing (OFDM), the waveform designed by CP-OTFS AFS demonstrates superior interference resilience, and the beamforming designed by SDR-BD strikes a balance in the overall performance of the ISWSCPTS. Full article

In an increasingly globalized world, the intelligent extraction of maritime targets is crucial for both military defense and maritime traffic monitoring. The flexibility and cost-effectiveness of unmanned aerial vehicles (UAVs) in remote sensing make them invaluable tools for ship extraction. Therefore, this paper introduces a training-free, highly accurate, and stable method for ship extraction in UAV remote sensing images. First, we present the dynamic tracking matched filter (DTMF), which leverages the concept of time as a tuning factor to enhance the traditional matched filter (MF). This refinement gives DTMF superior adaptability and consistent detection performance across different time points. Next, the DTMF method is rigorously integrated into a recurrent neural network (RNN) framework using mathematical derivation and optimization principles. To further improve the convergence and robust of the RNN solution, we design an adaptive feedback recurrent neural network (AFRNN), which optimally solves the DTMF problem. Finally, we evaluate the performance of different methods based on ship extraction accuracy using specific evaluation metrics. The results show that the proposed methods achieve over 99% overall accuracy and KAPPA coefficients above 82% in various scenarios. This approach excels in complex scenes with multiple targets and background interference, delivering distinct and precise extraction results while minimizing errors. The efficacy of the DTMF method in extracting ship targets was validated through rigorous testing. Full article

Remote passive sonar detection with underwater acoustic vector sensor (UAVS) has attracted increasing attention due to its merit in measuring the full sound field information. However, the accurate estimation of the direction-of-arrival (DOA) remains a challenging problem, especially under low signal-to-noise ratio (SNR) conditions. In this paper, a novel convolution (COV)-based single-vector acoustic preprocessing method is proposed on the basis of the single-vector acoustic preprocessing model. In view of the theoretical analysis of the classical single-vector acoustic DOA estimation method, the principle of preprocessing can be described as “to achieve an improved denoising performance in the constraint of equivalent amplitude gain and phase response.” This can be naturally guaranteed by our proposed COV method. In addition, the upper bound with matched filtering (MF) preprocessing is provided in the consideration of the optimal linear signal processing for weak signal detection under Gaussian noise. Numerical analyses demonstrate the effectiveness of our proposed preprocessing method with both vector array signal processing-based and intensity-based methods. Experimental verification conducted in South China Sea further verifies the effectiveness of our approach for practical applications. This work can lay a solid foundation in improving underwater remote vector acoustic DOA estimation under low SNR, and can provide important guidance for future research work. Full article

Differences between conventional sonar and Multiple-Input Multiple-Output (MIMO) sonar systems arise in achieving high angular and range resolution. MIMO sonar uses Matched Filtering (MF) with well-correlated transmitted signals to enhance spatial resolution by obtaining virtual arrays. However, imperfect correlation characteristics yield high sidelobe values, which hinder accurate target localization in underwater imagery. To address this, a Compressed Sensing (CS) method is proposed by reconstructing echo signals to suppress correlation noise between orthogonal waveforms. A shifted dictionary matrix and a deterministic Discrete Fourier Transform (DFT) measurement matrix are used to multiply received echo signals to yield compressed measurements. A sparse recovery algorithm is applied to optimize signal reconstruction before joint transmit–receive beamforming forms a 2D sonar image in the angle-range domain. Numerical simulations and lake experimental results confirm the effectiveness of the proposed method, by obtaining a lower sidelobe sonar image under sub-Nyquist sampling rates as compared with other approaches. Full article