Search Results (68)

The performance of the human eye is limited not only by optical factors but also capabilities of signal processing. The maximum spatial frequency that can be reliably processed depends on the sampling rate. If this frequency is exceeded, spatial aliasing occurs. In this study, we investigate the optimum amount of defocus and spherical aberration needed to avoid spatial aliasing. Measurements are carried out using a simple model eye with the optical and geometrical parameters close to those of a living human eye. A checkerboard pattern with the spatial frequency of 60 cycles/degree is used as a stimulus. A deformable mirror was used to control the amount of defocus and spherical aberration from 0 µm to 0.50 µm in steps of 0.05 µm. If the amount of aberrations is low, fringes of aliased signals are visible along the direction 35.5 degrees relative to the vertical edge of the image. This direction is close to the diagonal direction along which the sampling rate is the lowest. When the amount of aberrations reaches 0.45 µm, spatial aliasing is not observed. The results suggest that low amount of ocular aberrations is desired. Full article

Passive acoustic sensing with arrays has applications in many fields, including atmospheric monitoring of low frequency sounds (i.e., infrasound). Beamforming of array signals to gain spatial information about the signal is common, but the performance is often degraded due to limited resources (e.g., number of sensors, array size). Such sparse arrays create ambiguities due to reduced resolution and spatial aliasing. While previous work has focused on either maximizing array resolution or minimizing spatial aliasing, the current study demonstrates how evolutionary algorithms can be utilized to identify array configurations that optimize for both properties. The non-dominated sorting genetic algorithm II (NSGA-II) was used with the beamwidth and maximum sidelobe level as the fitness functions to iteratively identify a group of optimized synthesized array configurations. This group is termed a Pareto-front and is optimized such that one fitness function cannot be improved without a decrease in the other. These optimized solutions were studied for a single frequency (8 Hz) and a multi-frequency (3 to 20 Hz) signal using either a 36-element or 9-element array with a 60 m aperture. The performance of the synthesized arrays was compared against established array configurations (baseline) with most of the Pareto-front solutions outperforming these baseline configurations. The largest improvements to array performance using the synthesized configurations were with fewer array elements and the multi-frequency signal. Full article

Ultra-high-resolution synthetic aperture radar (SAR) has important applications in military and civilian fields. However, the acquisition of high-resolution SAR imagery poses considerable processing challenges, including limitations in traditional slant range model precision, the spatial variation in equivalent velocity, spectral aliasing, and non-negligible error introduced by stop-and-go assumption. To this end, this paper proposes an improved extended wavenumber domain imaging algorithm for ultra-high-resolution SAR to systematically address the imaging quality degradation caused by these challenges. In the proposed algorithm, the one-step motion compensation method is employed to compensate for the errors caused by orbital curvature through range-dependent envelope shift interpolation and phase function correction. Then, the interpolation based on modified Stolt mapping is performed, thereby facilitating effective separation of the range and azimuth focusing. Finally, the residual range cell migration correction is applied to eliminate range position errors, followed by azimuth compression to achieve high-precision focusing. Both simulation and spaceborne data experiments are performed to verify the effectiveness of the proposed algorithm. Full article

High-dynamic airborne gravimetry faces critical challenges from platform-induced noise contamination. Conventional filtering methods exhibit inherent limitations in simultaneously achieving dynamic tracking capability and spectral fidelity. To overcome these constraints, this study proposes a Kalman–FIR fusion filtering (K-F) method, which is validated through engineering implementation on the GIPS-1A airborne gravimeter platform. The proposed framework employs a dual-stage strategy: (1) An adaptive state-space framework employing calibration coefficients (S_x, S_y, S_z) continuously estimates triaxial acceleration errors to compensate for gravity anomaly signals. This approach resolves aliasing artifacts induced by non-stationary noise while preserving low-frequency gravity components that are traditionally attenuated by conventional FIR filters. (2) A window-optimized FIR post-filter explicitly regulates cutoff frequencies to ensure spectral compatibility with downstream processing workflows, including terrain correction. Flight experiments demonstrate that the K-F method achieves a repeat-line internal consistency of 0.558 mGal at 0.01 Hz—a 65.3% accuracy improvement over standalone FIR filtering (1.606 mGal at 0.01 Hz). Concurrently, it enhances spatial resolution to 2.5 km (half-wavelength), enabling the recovery of data segments corrupted by airflow disturbances that were previously unusable. Implemented on the GIPS-1A system, K-F enables precision mineral exploration and establishes a noise-suppressed paradigm for extreme-dynamic gravimetry. Full article

Lane detection plays a fundamental role in autonomous driving systems, yet it remains challenging under complex real-world conditions such as low illumination, occlusion, and degraded lane markings. In this paper, we propose a novel lane detection framework, Informative Feature Pyramid Network (Info-FPNet), designed to improve multi-scale feature representation and alignment for robust lane detection. Specifically, the proposed architecture integrates two key modules: an informative feature pyramid (IFP) module and a cross-layer refinement (CLR) module. The IFP module selectively aggregates spatially and semantically informative features across different scales using pixel shuffle upsampling, feature alignment, and semantic encoding mechanisms, thereby preserving fine-grained details and minimizing aliasing effects. The CLR module applies region-wise attention and anchor regression to refine coarse lane proposals, enabling better localization of curved or occluded lanes. Experimental results on two public benchmarks, CULane and TuSimple, demonstrate that the proposed Info-FPNet outperforms state-of-the-art approaches in terms of F1 score and is robust under challenging conditions such as nighttime, strong reflections, and occlusions. Furthermore, the proposed method maintains real-time inference speed and low computational overhead, validating its effectiveness and practicality in real-world applications. Full article

Accurate flood forecasts are imperative to supervise and prepare for extreme events to assess the risks and develop proactive prevention strategies. The flood time-series data exhibit both spatial and temporal structures and make it challenging for the models to fully capture the embedded features due to their complex stochastic nature. This paper proposed a new approach for the first time using variational mode decomposition (VMD) hybridized with Gaussian process regression (GPR) to design the VMD-GPR model for daily flood forecasting. First, the VMD model decomposed the (t − 1) lag into several signals called intrinsic mode functions (IMFs). The VMD has the ability to improve noise robustness, better mode separation, reduced mode aliasing, and end effects. Then, the partial auto-correlation function (PACF) was applied to determine the significant lag (t − 1). Finally, the PACF-based decomposed IMFs were sent into the GPR to forecast the daily flood index at (t − 1) for Jeddah and Jazan stations in Saudi Arabia. The long short-term memory (LSTM) boosted regression tree (BRT) and cascaded forward neural network (CFNN) models were combined with VMD to compare along with the standalone versions. The proposed VMD-GPR outperformed the comparing model to forecast daily floods for both stations using a set of performance metrics. The VMD-GPR outperformed comparing models by achieving R = 0.9825, RMSE = 0.0745, MAE = 0.0088, E_NS = 0.9651, KGE = 0.9802, IA = 0.9911, U_95% = 0.2065 for Jeddah station, and R = 0.9891, RMSE = 0.0945, MAE = 0.0189, E_NS = 0.9781, KGE = 0.9849, IA = 0.9945, U_95% = 0.2621 for Jazan station. The proposed VMD-GPR method efficiently analyzes flood events to forecast in these two stations to facilitate flood forecasting for disaster mitigation and enable the efficient use of water resources. The VMD-GPR model can help policymakers in strategic planning flood management to undertake mandatory risk mitigation measures. Full article

Microwave camera provides 3-D high-resolution radar images at video frame rates, enabling the capture of dynamic target features. Multiple-input–multiple-output (MIMO) array-based 3-D radar imaging system requires fewer antennas, which effectively reduces hardware costs. Due to the limited computational resources of the miniaturized MIMO microwave camera, real-time processing of a large amount of 3-D echo data requires an imaging algorithm that has both real-time performance and large output spatial extent. This paper presents the limited output spatial extent and spatial aliasing in existing MIMO wavenumber domain algorithms through theoretical derivation and simulation. To suppress aliasing while expanding the output spatial extent, an optimization approach for the wavenumber domain algorithms is proposed. The improved wavenumber domain algorithms divide the target area into multiple sub-blocks, and a broader range of imaging results is obtained through independent imaging of the sub-blocks and a spatial aliasing suppression filter. Simulation results show that the improved wavenumber domain algorithms effectively suppress the aliasing energy of each sub-block while maintaining the advantage of low time complexity. Expansion of output spatial extent in existing MIMO wavenumber domain algorithms is achieved. Full article

High-resolution lunar gravity anomaly data are of great significance for the study of the lunar crust and lithosphere structure, asymmetric thermal evolution, impact basin subsurface structure and mass tumor genesis, breccia, and magmatism. However, due to errors in satellite orbit and instrument observation, correlation error in high-order spherical harmonic coefficients, and other factors, satellite observation gravity anomaly data present evident aliasing phenomena of stripe noise and random noise in the spatial domain, resulting in difficulties in practical application analysis. In this paper, a lunar satellite gravity anomaly denoising method based on prior knowledge deep learning is proposed. In one instance, the prior knowledge is fused into the data set, the manual processing results are labeled, and the six label-superimposed directions of the simulated stripe noise are used as the sample input data. Conversely, because the gravity field is a harmonic field with smooth characteristics, the Laplace constraint is added to the loss function, and the deep learning results are optimized through Gaussian filtering. Synthetic and real data tests demonstrate the effectiveness of the proposed method in removing complex noise from lunar satellite gravity anomaly data. Full article

Linear noise, a significant type of interference in exploration seismic data, adversely affects the signal-to-noise ratio (SNR) and imaging resolution. As seismic exploration advances, the constraints of the acquisition environment hinder the ability to acquire seismic data in a regular and dense manner, complicating the suppression of linear noise. To address this challenge, we have developed an anti-aliasing and anti-leakage frequency–wavenumber (f-k) filtering method. This approach effectively mitigates issues of spatial aliasing and spectral leakage caused by irregular coarse data acquisition by integrating linear moveout correction and anti-leakage Fourier transform into traditional f-k filtering. The efficacy of our method was demonstrated through examples of linear noise suppression on both irregular coarse synthetic data and field seismic data. Full article

Spatial heterodyne spectroscopy (SHS) based on prism dispersion is a novel technique designed to overcome the limitations of traditional grating-based SHS, which is affected by grating diffraction. However, there are still some challenges with this technique, one of which is that the fringe localization plane (FLP) moves with changes in wavelength. This paper proposes a prism-based tunable SHS where the FLP is fixed, utilizing prism–bimirror–mirror structures. The theoretical spectral resolving power, based on an example, is higher than 1300 in the spectral range from 10,000 cm⁻¹ to 25,641 cm⁻¹ and is approximately 27,595 at 25,641 cm⁻¹. Furthermore, we propose solutions to simplify the motion control system and address the problem of spectral aliasing. Full article

Hyperspectral video acquisition requires a precise balance between spectral and temporal resolution, often achieved through compressive sampling using two-dimensional detectors and spectral reconstruction algorithms. However, the reliance on spatial light modulators for coding reduces optical efficiency, while complex recovery algorithms hinder real-time reconstruction. To address these challenges, we propose a digital-micromirror-device-based complementary dual-channel hyperspectral (DMD-CDH) video imaging system. This system employs a DMD for simultaneous light splitting and spatial encoding, enabling one channel to perform non-aliasing spectral sampling at lower frame rates while the other provides complementary high-rate sampling for panchromatic video. Featuring high optical throughput and efficient complementary sampling, the system ensures reliable hyperspectral video reconstruction and serves as a robust ground-based validation platform for remote sensing applications. Additionally, we introduce tailored optical error calibration and fixation techniques alongside a lightweight hyperspectral fusion network for reconstruction, achieving hyperspectral frame rates exceeding 30 fps. Compared to the existing models, this system simplifies the calibration process and provides a practical high-performance solution for real-time hyperspectral video imaging. Full article

Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis. Full article

Understanding the spatiotemporal dynamics of atmospheric PM_2.5 concentration is highly challenging due to its evolution processes have complex and nonlinear patterns. Traditional mode decomposition methods struggle to accurately capture the mode features of PM_2.5 concentrations. In this study, we utilized the global linearization capabilities of the Koopman method to analyze the hourly and daily spatiotemporal processes of PM_2.5 concentration in the Beijing–Tianjin–Hebei (BTH) region from 2019 to 2021. This approach decomposes the data into the superposition of different spatial modes, revealing their hierarchical spatiotemporal structure and reconstructing the dynamic processes. The results show that PM_2.5 concentrations exhibit high-frequency cycles of 12 and 24 h, as well as low-frequency cycles of 124 and 353 days, while also revealing spatiotemporal modes of growth, recession, and oscillation. The superposition of these modes enables the reconstruction of spatiotemporal dynamics with a mean absolute percentage error (MAPE) of only 0.6%. Unlike empirical mode decomposition (EMD), Koopman mode decomposition (KMD) method avoids mode aliasing and provides a clearer identification of global and key modes compared to wavelet analysis. These findings underscore the effectiveness of KMD method in analyzing and reconstructing the spatiotemporal dynamics of PM_2.5 concentration, offering new insights into the understanding and reconstruction of other complex spatiotemporal phenomena. Full article

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method. Full article

In the last few years, many space surveillance initiatives have started to consider the problem represented by resident space object overpopulation. In particular, the European Space Surveillance and Tracking (EUSST) consortium is in charge of providing services like collision avoidance, fragmentation analysis, and re-entry, which rely on measurements obtained through ground-based sensors. BIRALES is an Italian survey radar belonging to the EUSST framework and is capable of providing measurements including Doppler shift, slant range, and angular profile. In recent years, the Music Approach for Track Estimate and Refinement (MATER) algorithm has been developed to retrieve angular tracks through an adaptive beamforming technique, guaranteeing the generation of more accurate and robust measurements with respect to the previous static beamforming approach. This work presents the design of a new data processing chain to be used by BIRALES to compute the angular track. The signal acquired by the BIRALES receiver array is down-converted and the receiver bandwidth is split into multiple channels, in order to maximize the signal-to-noise ratio of the measurements. Then, the signal passes through a detection block, where an isolation procedure creates, for each epoch, signal correlation matrices (CMs) related to the channels involved in the detection and then processes them to isolate the data stream related to a single detected source. Consequently, for each epoch and for each detected source, just the CM featuring the largest signal contribution is kept, allowing deriving the Doppler shift measurement from the channel illumination sequence. The MATER algorithm is applied to each CM stream, first estimating the signal directions of arrival, then grouping them in the observation time window, and eventually returning the target angular track. Ambiguous estimates may be present due to the configuration of the receiver array, which cause spatial aliasing phenomena. This problem can be addressed by either exploiting transit prediction (in the case of cataloged objects), or by applying tailored criteria (for uncatalogued objects). The performance of the new architecture was assessed in real operational scenarios, demonstrating the enhancement represented by the implementation of the channelization strategy, as well as the angular measurement accuracy returned by MATER, in both nominal and off-nominal scenarios. Full article