This paper reports a simulation study of the two-dimensional (2D) phase structure of high-frequency (HF) skywave wavefronts arriving at ground level after single-hop oblique ionospheric reflection, bounding the aperture over which a single-radius spherical wavefront model remains consistent for distributed long-baseline ground-based HF receiver arrays. For a representative short-range link (transmitter at Beijing; ground range 419 km; 10 MHz; IRI-2020 epoch 2026-04-14T06:00 UTC), three-dimensional IONORT ray tracing produces a dense landing-phase footprint fitted to a 2D second-order Taylor surface with residual below 1.5%. Two contributions to the principal-curvature splitting are decomposed. The oblique-projection component yields a free-space geometric astigmatic ratio determined solely by the arrival elevation, while ionospheric refraction acts as an anisotropic cylindrical lens, compressing the longitudinal curvature radius and expanding the transverse one. The phase mismatch between the simulated anisotropic wavefront and a single-radius isotropic reference grows quadratically with aperture extent. A closed-form expression for the aperture at which this mismatch reaches a half-power coherence-degradation threshold yields a scale of approximately 9 km for the present configuration. These results are presented as a configuration-specific case study whose analytic forms are intended to be portable.

Micro-scanning thermal microscope imaging system (MTMIS) enables non-destructive observation of temperature variations in fine targets and has important applications in microelectronic detection and biomedical diagnosis. However, existing dual-band systems suffer from low resolution, poor fusion quality, and noise interference, limiting effective utilization of complementary infrared and visible information. To address these issues, this paper proposes a dual-band image fusion and super-resolution (SR) reconstruction network (TCD-KDnet) based on knowledge distillation. A teacher network generates high-resolution fused images and intermediate feature maps as pseudo-labels to guide the student network, which integrates a fusion module and an SR reconstruction module. In particular, a Transformer-CNN (convolutional neural network) dual-branch fusion method is designed to capture global cross-modal context and local texture details, enhanced by hierarchical fusion and attention mechanisms. Furthermore, an improved lightweight RCAN is adopted for efficient SR reconstruction with reduced parameters. Experimental results on public datasets and real MTMIS data demonstrate that the proposed method significantly improves image quality, detail preservation, and reconstruction efficiency.

Infrared target detection using unmanned aerial vehicles (UAVs) presents a frontier in intelligent infrared recognition with diverse applications. However, challenges such as complex noise interference and drastic target scale variations hinder practical implementation. This study introduces an infrared target detection algorithm that integrates anti-interference and multi-scale perception mechanisms. The algorithm features a vision Transformer(ViT)-based anti-interference feature encoder and a multi-scale feature encoder with deformable attention. Experimental results on the HIT-UAV and VT-MOT datasets demonstrate a 4.5 and 5.3 percentage point improvement in small-target detection accuracy, respectively, highlighting its robust anti-interference capabilities and high practical applicability.

Hyperspectral (HS) and multispectral (MS) image registration is an essential precondition for their joint exploitation, yet remains challenging under their large spatial-spectral resolution discrepancy. A cross-resolution teacher-student framework (CTSF) is proposed in this paper, where HS and MS image registration is degraded as cross-resolution single-band registration. Especially, a hierarchical shared teacher-student network is designed to learn paired descriptors from these cross-resolution pairs. More specifically, a dual-path hierarchical shared teacher and a single-path hierarchical student net are proposed to reduce gaps of content representation and receptive field between cross-resolution pairs. Furthermore, a confidence-guided selective distillation strategy and a cross-resolution alignment regularization are investigated to perform high-consistency description. Experimental results demonstrate that our CTSF shows obvious advantages as the resolution gap grows.

The multiple signal classification (MUSIC) algorithm is a super-resolution technique whose angle estimation accuracy is fundamentally constrained by the signal-to-noise ratio (SNR) and the observation baselines. A higher SNR ensures a more stable partitioning of the noise subspace, while a greater number of observation baselines yield a larger equivalent synthesized SNR gain, thereby enhancing angle estimation precision. The angle estimation accuracy of traditional interferometric MUSIC processing is limited by the single-antenna received SNR and the number of receiving antennas per sub-aperture. To address this, this paper proposes a sparse array MUSIC based high-resolution passive localization method. This approach effectively integrates the physical gain of sparse array synthesis processing with the super-resolution capability of the MUSIC algorithm. By leveraging sparse array processing, the input conditions for the MUSIC algorithm are simultaneously improved in two dimensions: the observation matrix SNR and the observation baselines, which significantly enhances direction of arrival (DOA) estimation accuracy. Using a T-type sparse array configuration, this paper details the algorithmic derivation and simulations. Furthermore, a closed-form Cramér-Rao bound (CRB) expression for the sparse array MUSIC algorithm is derived, and simulation results are presented to compare the CRB performance of the proposed method against traditional MUSIC approaches.

In modern electronic reconnaissance, passive localization plays a crucial role in target positioning and tracking in complex electromagnetic environments due to its inherent concealment capability. However, conventional multistation localization methods often suffer from large estimation errors and limited robustness in dynamic scenarios because of their reliance on multi-snapshot measurements. To address these limitations, this paper extends the backward projection positioning (BPP) method to distributed radar systems for airborne target localization and tracking. By performing backward projection on spatial-domain Doppler history, a cost function involving azimuth angle, elevation angle, and slant range is constructed to generate high-resolution localization images. The distributed radar system forms a physical aperture, enabling high-precision and robust tracking performance. To reduce ambiguities caused by sparse deployment, an optimization strategy based on a genetic algorithm is proposed for radar node placement. The performance of the proposed method is further evaluated using a linear distributed configuration. Simulation results demonstrate that the proposed approach achieves approximately an order-of-magnitude improvement in localization accuracy compared with conventional DOA-based methods.

Passive synthetic aperture (PSA) positioning method can achieve high precision and resolution in passive location. However, existing approaches suffer from insufficient adaptability to non-ideal targets. In practical reconnaissance scenarios, complex pulse repetition interval (PRI) modulation and periodic beam scanning of radar emitters inevitably degrade PSA positioning performance. Specifically, periodic scanning causes block sparse observations and grating lobes, while nonuniform PRI disrupts phase coherence across the synthetic aperture, resulting in spectral leakage and elevated sidelobes. By exploiting the approximate linear frequency modulation (LFM) characteristic and fractional-domain sparsity of Doppler signal under broadside and small squint conditions, a fractional-domain reconstruction method based on compressed sensing (CS) for PSA positioning is proposed. A nonuniform sensing matrix is constructed at the true sampling instants, and the full-aperture Doppler phase history is reconstructed via orthogonal matching pursuit (OMP) for coherent accumulation positioning. Simulations under uniform, stagger, and jitter PRI conditions demonstrate effective suppression of grating lobes and spurious peaks, together with improved image contrast and localization accuracy over conventional PSA positioning.

This paper proposes a multi-station long baseline interferometry passive localization method. Multiple stations simultaneously receive target signals, and through inter-station differential interferometry processing, effective signal de-modulation is achieved. Through the relative motion between observation stations and the mission area, two-dimensional sampling of the target radiation signal spatial spectrum is realized, enabling inversion of the target distribution and obtaining two-dimensional ultra-high-precision positioning results. This paper models the multi-station long baseline interferometry scenario, theoretically derives the correspondence between the target distribution map and the bi-static interferometry data, presents the processing flow of the multi-station long baseline interferometry passive localization method, analyzes the positioning accuracy of this method, and verifies through simulation that the method possesses high-resolution positioning capability.

To address the degradation of global navigation satellite system (GNSS) positioning performance in challenging environments such as signal blockage and interference, this paper investigates a high-precision positioning method based on Starlink satellite signals of opportunity. A self-developed receiving platform is used to receive and analyze a Starlink downlink narrowband beacon signal at 11.45 GHz. To overcome the challenges of unknown prior signal parameters, unknown prior frequency offset under low-SNR (signal-to-noise ratio) conditions, and the difficulty of robustly estimating the reference time, a boundary-constrained integrated stationary phase method (BISPM) is proposed to achieve joint and accurate estimation of the reception time and carrier frequency. On this basis, a tomographic synthetic aperture positioning model based on virtual steering vectors is constructed. By coherently integrating short-time observations and performing back-projection imaging, the proposed method improves positioning resolution under single-satellite conditions. Experimental results using the actually received signal from STARLINK-32494 show that, with a synthetic aperture time of 3 s and only a single satellite, positioning accuracies of approximately 0.4 m in azimuth and 8 m in range can be achieved. These results verify the feasibility of using Starlink narrowband beacon signals for opportunistic positioning and provide a useful reference for rapid and high-precision positioning with low-Earth-orbit satellite signals of opportunity.

To suppress high grating lobes and reduce azimuth ambiguity caused by intermittent interception of periodically scanning emitters, an element amplitude-weight optimization method is proposed. Grating-lobe suppression is formulated as an amplitude optimization problem for a block-sparse virtual array. A fitness function considering sidelobe suppression and mainlobe preservation is constructed, and an improved differential evolution algorithm is used to search for the amplitude weights. Simulation results show that the proposed method suppresses high grating lobes near the mainlobe, increases the peak-discrimination margin, and improves the localization stability of the backward projection positioning (BPP) method under sparse reception.