Search Results (778)

Ubiquitous digital array radar (UDAR) extends the integration time of moving targets by deploying a wide transmitting beam and multiple narrow receiving beams to cover the entire observed airspace. By exchanging time for energy, it effectively improves the detection ability for weak targets. Nevertheless, target motion introduces severe across-range unit (ARU), across-Doppler unit (ADU), and across-beam unit (ABU) effects, dispersing target energy across the range–Doppler-beam space. This paper proposes a beam domain angle rotation compensation and keystone-matched filtering (BARC-KTMF) algorithm to address the “three-crossing” challenge. This algorithm first corrects ABU by rotating beam–domain coordinates to align scattered energy into the final beam unit, reshaping the signal distribution pattern. Then, the KTMF method is utilized to focus target energy in the time-frequency domain. Furthermore, a special spatial windowing technique is developed to improve computational efficiency through parallel block processing. Simulation results show that the proposed approach achieves an excellent signal-to-noise ratio (SNR) gain over the typical single-beam and multi-beam long-time coherent integration (LTCI) methods under low SNR conditions. Additionally, the presented algorithm also has the capability of coarse estimation for the target incident angle. This work extends the LTCI technique to the beam domain, offering a robust framework for high-speed weak target detection. Full article

The coherency matrix serves as a valuable tool for explaining the intricate details of various terrain targets. However, a significant challenge arises when analyzing ground targets with similar scattering characteristics in polarimetric synthetic aperture radar (PolSAR) target decomposition. Specifically, the overestimation of volume scattering (OVS) introduces ambiguity in characterizing the scattering mechanism and uncertainty in deciphering the scattering mechanism of large oriented built-up areas. To address these challenges, based on the generalized five-component decomposition (G5U), we propose a hierarchical extension of the G5U method, termed ExG5U, which incorporates orientation and phase angles into the matrix rotation process. The resulting transformed coherency matrices are then subjected to a five-component decomposition framework, enhanced with four refined volume scattering models. Additionally, we have reformulated the branch conditions to facilitate more precise interpretations of scattering mechanisms. To validate the efficacy of the proposed method, we have conducted comprehensive evaluations using diverse PolSAR datasets from Gaofen-3, Radarsat-2, and ESAR, covering varying data acquisition timelines, sites, and frequency bands. The findings indicate that the ExG5U method proficiently captures the scattering characteristics of ambiguous regions and shows promising potential in mitigating OVS, ultimately facilitating a more accurate portrayal of scattering mechanisms of various terrain types. Full article

With the rapid development of drone technology, target detection and estimation of radar parameters for maneuvering targets have become crucial. Drones, with their small radar cross-sections and high maneuverability, cause range migration (RM) and Doppler frequency migration (DFM), which complicate the use of traditional radar methods and reduce detection accuracy. Furthermore, the detection of multiple targets exacerbates the issue, as target interference complicates detection and impedes parameter estimation. To address this issue, this paper presents a method for high-resolution multi-drone target detection and parameter estimation based on the adjacent cross-correlation function (ACCF), fractional Fourier transform (FrFT), and deep learning techniques. The ACCF operation is first utilized to eliminate RM and reduce the higher-order components of DFM. Subsequently, the FrFT is applied to achieve coherent integration and enhance energy concentration. Additionally, a convolutional neural network (CNN) is employed to address issues of spectral overlap in multi-target FrFT processing, further improving resolution and detection performance. Experimental results demonstrate that the proposed method significantly outperforms existing approaches in probability of detection and accuracy of parameter estimation for multiple maneuvering targets, underscoring its strong potential for practical applications. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

Using interferometric synthetic aperture radar (InSAR) to observe slow ground deformation can be challenging due to many sources of error, with tropospheric phase delay and unwrapping errors being the most significant. While analytical methods, weather models, and data exist to mitigate tropospheric error, most of these techniques are unsuitable for all InSAR applications (e.g., complex tropospheric mixing in the tropics) or are deficient in spatial or temporal resolution. Likewise, there are methods for removing the unwrapping error, but they cannot resolve the true phase when there is a high prevalence (>40%) of unwrapping error in a set of interferograms. Applying tropospheric delay removal techniques is unnecessary for C-band Sentinel-1 InSAR time series studies, and the effect of unwrapping error can be minimized if the full dataset is utilized. We demonstrate that using interferograms with long temporal baselines (800 days to 1600 days) but very short perpendicular baselines (<5 m) (LTSPB) can lower the velocity detection threshold to 2 mm y⁻¹ to 3 mm y⁻¹ for long-term coherent permanent scatterers. The LTSPB interferograms can measure slow deformation rates because the expected differential phases are larger than those of small baselines and potentially exceed the typical noise amplitude while also reducing the sensitivity of the time series estimation to the noise sources. The method takes advantage of the Sentinel-1 mission length (2016 to present), which, for most regions, can yield up to 300 interferograms that meet the LTSPB baseline criteria. We demonstrate that low velocity detection can be achieved by comparing the expected LTSPB differential phase measurements to synthetic tests and tropospheric delay from the Global Navigation Satellite System. We then characterize the slow (~3 mm/y) ground deformation of the Socorro Magma Body, New Mexico, and the Tampa Bay Area using LTSPB InSAR analysis. The method we describe has implications for simplifying the InSAR time series processing chain and enhancing the velocity detection threshold. Full article

Image matching between spaceborne synthetic aperture radar (SAR) images are frequently interfered with by speckle noise, resulting in low matching accuracy, and the vast coverage of SAR images renders the direct matching approach inefficient. To address this issue, the study puts forward a multi-scale adaptive improved SAR image block matching method (called STSU–SAR–SIFT). To improve accuracy, this method addresses the issue of the number of feature points under different thresholds by using the SAR–Shi–Tomasi response function in a multi-scale space. Then, the SUSAN function is used to constrain the effect of coherent noise on the initial feature points, and the multi-scale and multi-directional GLOH descriptor construction approach is used to boost the robustness of descriptors. To improve efficiency, the method adopts the main and additional image overlapping area matching method to reduce the search range and uses multi-core CPU+GPU collaborative parallel computing to boost the efficiency of the SAR–SIFT algorithm by block processing the overlapping area. The experimental results demonstrate that the STSU–SAR–SIFT approach presented in this paper has better accuracy and distribution. After the algorithm acceleration, the efficiency is obviously improved. Full article

The demand for versatility and finer resolution drives phased array radars to develop towards multi-band operating. However, the bandwidth limitations of conventional electronic devices make multi-band manipulation of frequency and phase rather challenging. This paper introduces a joint phase–frequency distribution manipulation method. By introducing a time delay line after optical pulses, the frequency conversion and phase shift are tightly coupled. Then, the phase–frequency–time mapping for multi-band signals in a single phased array system is established. The generation, transmission, and reception of multi-band signals are simultaneously achieved. Our approach enables multi-band frequency conversion and phase shifting in a single hardware framework, ensuring synchronization and coherence across multiple bands. We experimentally demonstrate the generation, frequency conversion, and phase control of signals across four bands (S, X, Ku, and K). Beamforming and data fusion of four-band linear frequency-modulated signals with a total bandwidth of 4 GHz are achieved, resulting in a four-fold improvement in range resolution. It is also verified that the number of bands and total bandwidth can be further expanded through channel interleaving. Full article

Interrupted sampling repeater jamming (ISRJ) is characterized by its coherent processing gains and flexible modulation techniques. ISRJ generates spurious targets along the range, which presents significant challenges to the radar systems. However, existing ISRJ countermeasure methods struggle to eliminate ISRJ signals without compromising the integrity of the real target signal, especially under low-signal-to-noise-ratio (SNR) conditions, resulting in a deteriorated sidelobe and diminished detection performance. We propose a complex-valued encoder–decoder network (CVEDNet) to address these challenges based on signal decomposition. This network offers an end-to-end ISRJ suppression approach, working on complex-valued time-domain signals without the need for additional preprocessing. The encoding and decoding structure suppresses noise components and obtains more compact echo feature representations through layer-by-layer compression and reconstruction. A stacked dual-branch structure and multi-scale dilated convolutions are adopted to further separate the echo signal and ISRJ based on high-dimensional features. A multi-domain combined loss function integrates the waveform and range-pulse-compression information to ensure the amplitude and phase integrity of the reconstructed echo waveform during the training process. The effectiveness of the proposed method was validated in terms of its jamming suppression capability, echo fidelity, and detection performance indicators under low-SNR conditions compared to conventional methods. Full article

Wildfires pose a significant threat to the natural and built environment and may alter the hydrologic cycle in burned areas increasing the risk of flooding, erosion, debris flows, and shallow landslides. In this paper, we investigate the feasibility of using differential interferometric synthetic aperture radar (DInSAR) to interpret changes in ground surface elevation following the 2017 Eagle Creek Wildfire in Oregon, USA. We show that DInSAR is capable of measuring ground surface displacements in burned areas not obscured by vegetation cover and that interferometric coherence can differentiate between areas that experienced different burn severities. The distribution of projected vertical displacement was analyzed, suggesting that different areas experience variable rates of change, with some showing little to no change for up to four years after the fire. Comparison of the projected vertical displacements with cumulative precipitation and soil moisture suggests that increases in precipitation and soil moisture are related to periods of increased vertical displacement. The findings of this study suggest that DInSAR may have value where in situ instrumentation is infeasible and may assist in prioritizing areas at high-risk of erosion or other changes over large geographical extents and measurement locations for deployment of instrumentation. Full article

Radar echo extrapolation, a critical spatiotemporal sequence forecasting task, requires precise modeling of motion trajectories and intensity evolution from sequential radar reflectivity inputs. Contemporary deep learning implementations face two operational limitations: progressive attenuation of predicted echo intensities during autoregressive inference and spectral leakage-induced diffusion at high-intensity echo boundaries. This study presents RaDiT, a hybrid architecture combining differential transformer with adversarial training for radar echo extrapolation. The framework employs a U-Net backbone augmented with vision transformer blocks, utilizing differential attention mechanisms to govern spatiotemporal interactions. Our differential attention mechanism enhances noise suppression under high-threshold conditions, effectively minimizing spurious feature generation while improving metric reliability. A conditional GAN discriminator is integrated to maintain microphysical consistency in generated sequences, simultaneously addressing spectral blurring and intensity dissipation. Comprehensive evaluations demonstrate RaDiT’s superior performance in preserving spatiotemporal coherence and intensity across 0–90 min forecasting horizons. The proposed architecture achieves CSI improvements of 10.23% and 2.88% at 4 × 4 and 16 × 16 spatial pooling scales, respectively, for ≥30 dBZ thresholds on the CMARC dataset compared to PreDiff. To our knowledge, this represents the first successful implementation of differential transformers for radar echo extrapolation. Full article

Due to its fast processing time and robustness against harsh environmental conditions, the frequency modulated continuous waveform (FMCW) multiple-input multiple-output (MIMO) radar is widely used for target localization. For high-accuracy localization, the two-dimensional multiple signal classification (2D MUSIC) algorithm can be applied to signals received by a single FMCW MIMO radar, achieving high-resolution positioning performance. To further enhance estimation accuracy, received signals or MUSIC spectra from multiple FMCW MIMO radars are often collected at a data fusion center and processed coherently. However, this approach increases data communication overhead and implementation complexity. To address these challenges, we propose an efficient high-resolution target localization algorithm. In the proposed method, the target position estimates from multiple FMCW MIMO radars are collected and combined using a weighted averaging approach to determine the target’s position within a unified coordinate system at the data fusion center. We first analyze the achievable resolution in the unified coordinate system, considering the impact of local parameter estimation errors. Based on this analysis, weights are assigned according to the achievable resolution within the unified coordinate framework. Notably, due to the typically limited number of antennas in FMCW MIMO radars, the azimuth angle resolution tends to be relatively lower than the range resolution. As a result, the achievable resolution in the unified coordinate system depends on the placement of each FMCW MIMO radar. The performance of the proposed scheme is validated using both synthetic simulation data and experimentally measured data, demonstrating its effectiveness in real-world scenarios. Full article

Continuous and robust trajectory tracking of unmanned aerial vehicles (UAVs) plays a crucial role in urban air transportation systems. Accordingly, this article presents an end-to-end spatiotemporal correlation integration (ESCI)-based UAV tracking framework by leveraging a high-resolution cascade multiple input multiple output (MIMO) radar. On this account, a novel joint anti-interference detection and tracking system for weak extended targets is presented in this paper; the proposed method handles them jointly by integrating a continuous detection process into tracking. It not only eliminates the threshold decision-making process to avoid the loss of weak target information, but also significantly reduces the interference from other co-channel radars and strong clutters by exploring the spatiotemporal correlations within a sequence of radar frames, thereby improving the detectability of weak targets. In addition, to accommodate the time-varying number and extended size of radar reflections, with the ellipse spatial probability distribution model, the extended UAV with multiple scattering sources can be treated as an entity to track, and the complex measurement-to-object association procedure can be avoided. Finally, with Texas Instruments AWR2243 (TI AWR2243) we can utilize a cascade frequency-modulated continuous wave–multiple input multiple output (FMCW-MIMO) radar platform. The results show that the proposed method can obtain outstanding anti-interference performance for extended UAV tracking compared with state-of-the-art methods. Full article

Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines. Full article

This article presents a Synthetic-Aperture Distributed Phased Array (SADPA) framework to address emitter localization challenges in dynamic environments. Building on Distributed Synthetic-Aperture Radar (DSAR) principles, SADPA integrates distributed phased arrays with motion-induced phase compensation, enabling coherent aperture synthesis beyond physical array limits. By analytically modeling and compensating nonlinear phase variations caused by platform motion, we resolve critical barriers to signal integration while extending synthetic apertures. An improved MUSIC algorithm jointly estimates emitter positions and phase distortions, overcoming parameter coupling inherent in moving systems. To quantify fundamental performance limits, the Cramer–Rao bound (CRB) is derived as a theoretical benchmark. Numerical simulations demonstrate the SADPA framework’s superior performance in multi-source resolution and positioning accuracy; it achieves 0.012 m resolution at 10 GHz for emitters spaced 0.01 m apart. The system maintains consistent coherent gain exceeding 30 dB across both the 1.5 GHz communication and 10 GHz radar bands. Monte Carlo simulations further reveal that the MUSIC-DPD algorithm within the SADPA framework attains minimum positioning error (RMSE), with experimental results closely approaching the theoretical CRB. Full article

Frequency agile radar (FAR) exhibits robust anti-jamming capabilities and a superior low probability of intercept performance due to its randomized carrier frequency (CF) and pulse repetition frequency (PRF) hopping sequences. The advent of compressed sensing (CS) theory has effectively addressed the coherent processing challenges of frequency agile signals. Nonetheless, the reconstructed results often suffer from elevated sidelobe levels, which lead to significant sparse recovery errors. The performance of sparse reconstruction is greatly influenced by the correlation between the dictionary matrix columns. Specifically, weaker correlation usually means better target detection performance and lower false alarm probability. Consequently, this paper adopts the maximum coherence coefficient (MCC) between the dictionary matrix columns as the cost function. In addition, in order to reduce the correlation of the dictionary matrix and improve the target detection performance, a genetic algorithm (GA) is employed to jointly optimize the CF hopping coefficients and PRFs of the FAR. The echo of optimized signals is subsequently reconstructed using the alternating direction method of multipliers (ADMM) algorithm. Simulation results demonstrate the effectiveness of the proposal. Full article