Sparse Aperture Research Articles

An Enhanced Direct Position Determination of Mixed Circular and Non-Circular Sources Using Moving Virtual Interpolation Array.

In this study, a moving single-station direct position determination (DPD) algorithm based on virtual interpolated arrays is proposed. Existing moving single-station algorithms face challenges such as the incomplete utilization of sparse array apertures and insufficient consideration of mixed circular and non-circular signals. To address these issues, we propose an enhanced gridless DPD algorithm, suitable for multiple mixed circular and non-circular sources. Through constructing a non-zero unconjugated covariance matrix from the non-circular components of the mixed signals, the data dimensionality is expanded, and the gridless method is used to fill the voids in the coarray, significantly improving localization performance. Additionally, a unitary transformation method is applied to reduce computational complexity. This method transforms complex operations into real operations by applying unitary transformations to steering vectors and subspaces. Simulation results demonstrate that the proposed algorithm offers significant advantages in terms of array degrees of freedom and localization accuracy.

Mid-frequency contrast compensation for sparse aperture imaging systems by increasing the size of a sub-aperture

Mid-frequency contrast compensation for sparse aperture imaging systems by increasing the size of a sub-aperture

Multi-aperture telescopes at the quantum limit of superresolution imaging : Detecting subRayleigh object near a star

Angular resolution is a critical aspect of astronomical observation, as it determines the minimum resolvable angle between two objects. This is governed by the Rayleigh criterion, which states that the minimum resolvable angle is proportional to the wavelength (λ) over the diameter (D) of the aperture of a monolithic telescope. While larger aperture telescopes have advantages such as smaller angular resolution and higher sensitivity to dim and small exoplanets, they also have disadvantages such as higher launch cost, design intricacies, and high mission cost. Using multi-aperture telescopes can be a cost-effective alternative as they work on the principles of baseline interferometry, making the minimum resolvable angle proportional to λ/B, where B is the baseline. In this work, we compare the performance of a single monolithic telescope to a multi-aperture alternative with the same effective glass area in the context of hypothesis testing between two scenarios - H1 (a star) and H2 (a star with an exoplanet). We formulate the theory based on likelihood ratio tests and find that the multi-aperture telescope performs better than the monolithic telescope when direct detection is used on the focal plane. While this is expected, the performance can be further improved by using quantum-inspired detecting strategies. We utilize Quantum Binary Spatial Mode Demultiplexing (BSPADE) to process the point spread function (PSF) of the telescopes and find better performance compared to the respective direct detection measurement. Therefore, our efforts can be viewed as one of the initial steps toward employing quantum-inspired detection techniques in sparse aperture configurations for high-contrast imaging applications. In conclusion, our work shows that multi-aperture telescopes are an effective alternative to monolithic telescopes for object discrimination and potentially for super-resolution imaging and their performance can be further improved by using quantum-inspired detection strategies. With their cost-effectiveness (see Appendix C) and potential for high performance, multi-aperture telescopes can significantly advance our ability to observe and study exoplanets and other celestial objects. Future research can explore ways to optimize the performance of multi-aperture telescopes and further improve their capabilities for astronomical observation, potentially facilitating the detection and imaging of Earth-like exoplanets.

Orthonormal aberration decomposition for circular aperture and rectangular field with extension to multiple-aperture systems

A new set of orthogonal polynomials and the traditional Zernike polynomials are combined to construct the wavefront of the sparse aperture (SA) optical system. The new set of orthogonal polynomials in the rectangular domain is derived based on the Zernike polynomials. The modulation transfer functions (MTFs) of the SA system under different fields of view are calculated. The effects of some wavefront terms on the field-related MTF are analyzed. Imaging simulation and restoration are conducted for the SA system. The results indicate that the wavefront of the SA system can be represented by the combination of the new set of orthogonal polynomials and the Zernike polynomials. The former represents the field term, and the latter represents the pupil term of the wavefront. The field-related Wiener filter is constructed to restore the imaging results of the SA system, and the results show that the image quality can be improved greatly through restoration.

Sparse Aperture ISAR Imaging and Cross-Range Scaling of Maneuvering Targets Based on Sparse CICPF Method

Sparse Aperture ISAR Imaging and Cross-Range Scaling of Maneuvering Targets Based on Sparse CICPF Method

Deep learning network with new weighting strategy for ISAR image enhancement

ABSTRACT In inverse synthetic aperture radar (ISAR) imaging, the conventional range-Doppler (RD) algorithm cannot obtain satisfactory imaging results for sparse aperture. Compressed sensing (CS) imaging methods, such as the typical iterative shrinkage and thresholding algorithm (ISTA), are often used in sparse aperture radar imaging. However, CS-based methods have high computational complexity and difficulty in setting parameters. To overcome the shortcomings, a new deep unfolding network named complex-valued weighted learning ISTA (CV-WLISTA) is proposed in this paper. The new network is very efficient and can learn the parameters adaptively. Based on ISTA and a new weighting strategy, it takes advantage of both the sparsity and the structural property of ISAR images to improve imaging performance. The experimental results show that CV-WLISTA has better reconstruction performance and higher efficiency compared with the traditional algorithms. Therefore, CV-WLISTA is an efficient ISAR imaging method with excellent performance.

A Fast Total-Variation Driven Network for Sparse Aperture ISAR Imaging

Conventional Fourier transform-based Inverse Synthetic Aperture Radar (ISAR) imaging cannot deal with the cases of incomplete radar echoes, in which the data needs to be specially processed by Sparse Aperture (SA) imaging methods. Most current SA-ISAR imaging employs sparsity-driven optimization methods. However, the assumption that the target reflectivity is sparse in the spatial domain is insufficient to capture the surface and edge features of the target if no sparse representations are found for the target and well incorporated in the imaging process. In view of the edge-preserving capability of Total Variation (TV) and the strong learning ability of the Deep Neural Network (DNN), we propose a TV-driven network to improve the SA ISAR imaging performance. We first develop a fast TV regularization method to perform the imaging where the Gradient Descent (GD) along with the Momentum Acceleration (MA) are incorporated to increase the computational efficiency. Then, we unwrap the iterative fast TV regularization into a cascaded neural network to make the key imaging parameters learnable, leading to improved imaging performance, which we refer to as FGDTV-Net. Experiments under a variety of scenarios show that the proposed FGDTV-Net for SA-ISAR imaging is superior to existing SA imaging algorithms in preserving surface and edge features and is more robust in low signal-to-noise scenarios.

Sparse SAR Imaging Algorithm in Marine Environments Based on Memory-Augmented Deep Unfolding Network

Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar (SAR) imaging rooted in sparse signal processing. Deep neural networks (DNNs), a current research emphasis, have, when integrated with sparse SAR, attracted notable attention for their exceptional imaging capabilities and high computational efficiency. Yet, the efficiency of traditional unfolding techniques is impeded by their architecturally inefficient design, which curtails their information transmission capacity and consequently detracts from the quality of reconstruction. This paper unveils a novel Memory-Augmented Deep Unfolding Network (MADUN) for SAR imaging in marine environments. Our methodology harnesses the synergies between deep learning and algorithmic unfolding, enhanced with a memory component, to elevate SAR imaging’s computational precision. At the heart of our investigation is the incorporation of High-Throughput Short-Term Memory (HSM) and Cross-Stage Long-Term Memory (CLM) within the MADUN framework, ensuring robust information flow across unfolding stages and solidifying the foundation for deep, long-term informational correlations. Our experimental results demonstrate that our strategy significantly surpasses existing methods in enhancing the reconstruction of sparse marine scenes.

A Sparse Aperture ISAR Imaging and Autofocusing Method Based on Meta-Learning Framework

A Sparse Aperture ISAR Imaging and Autofocusing Method Based on Meta-Learning Framework

Migration through Resolution Cell Correction and Sparse Aperture ISAR Imaging for Maneuvering Target Based on Whale Optimization Algorithm-Fast Iterative Shrinkage Thresholding Algorithm.

Targets faced by inverse synthetic aperture radar (ISAR) are often non-cooperative, with target maneuvering being the main manifestation of this non-cooperation. Maneuvers cause ISAR imaging results to be severely defocused, which can create huge difficulties in target identification. In addition, as the ISAR bandwidth continues to increase, the impact of migration through resolution cells (MTRC) on imaging results becomes more significant. Target non-cooperation may also result in sparse aperture, leading to the failure of traditional ISAR imaging algorithms. Therefore, this paper proposes an algorithm to realize MTRC correction and sparse aperture ISAR imaging for maneuvering targets simultaneously named whale optimization algorithm-fast iterative shrinkage thresholding algorithm (WOA-FISTA). In this algorithm, FISTA is used to perform MTRC correction and sparse aperture ISAR imaging efficiently and WOA is adopted to estimate the rotational parameter to eliminate the effects of maneuvering on imaging results. Experimental results based on simulation and measured datasets prove that the proposed algorithm implements sparse aperture ISAR imaging and MTRC correction for maneuvering targets simultaneously. The proposed algorithm achieves better results than traditional algorithms under different signal-to-noise ratio conditions.

Application of Sparse Synthetic Aperture Focusing Techniques to Ultrasound Imaging in Solids Using a Transducer Wedge.

This article discusses the application of sparse synthetic aperture focusing techniques (SAFTs) for fast and accurate ultrasonic nondestructive testing (NDT) imaging of solids in cases where a wedge is required between the transducer array and the test medium. A wedge is often used to appropriately direct the ultrasonic beams when testing for structural defects at particular orientations or when inspecting parts with particular geometries (e.g., waveguides). Both the virtual element (VE) and the plane-wave (PW) modalities of sparse-firing SAFT are examined for the wedge case that requires particular considerations in the beamforming algorithms for the wave refractions and mode conversions occurring at the wedge-medium interface. The method of wave mode compounding is also examined for this application to increase the array gain without increasing its physical aperture. Numerical simulations and experimental tests demonstrate the potential improvements in speed and accuracy obtainable by sparse SAFT adapted to wedge-transducer cases compared to a traditional full matrix capture (FMC) imaging mode. A practical implementation of the imaging of transverse defects in rail tracks is also presented.

Searching for low-mass companions at small separations in transition disks with aperture masking interferometry

Context. Transition disks have large central cavities that have been resolved by imaging surveys during recent years. Cavities and other substructures in circumstellar disks are often interpreted as signposts to massive companions. Detecting companions at small angular separations is challenging with coronagraphic imaging observations. Aims. We aim to search for stellar and substellar companions in the central regions of transition disks. Such companions could be responsible for the large dust-depleted cavities. We want to determine if these disks might be circumbinary in their nature, similar to the HD 142527 system. Methods. We observed four systems, HD 100453, HD 100546, HD 135344 B, and PDS 70, with the sparse aperture masking mode of VLT/SPHERE, also leveraging the star-hopping method with the adaptive optics system. We extracted the complex visibilities and bispectra from the H2 and H3 imaging data. A binary model was fit to the closure phases to search for companions and estimate detection limits. For validation, we also analyzed four archival datasets of HD 142527 and inferred the orbital elements and atmospheric parameters of its low-mass stellar companion. Results. We have not detected any significant point sources in the four observed systems. With a contrast sensitivity of ≈0.004, we can rule out stellar companions down to ≈2 au and partially explore the substellar regime at separations ≳3–5 au. The analysis of HD 142527 B revealed that its projected orbit is aligned with dust features in the extended inner disk and that the mutual inclination with the outer disk is close to coplanar for one of the two solutions. Atmospheric modeling confirms the low-gravity and slightly reddened spectral appearance (Teff ≈ 3300 K, log 𝑔 ≈ 3.7, and AV ≈ 0.7). The inferred and derived bulk parameters (log L*/L0 ≈ −0.65, M* ≈ 0.4 M⊙, and R* ≈ 1.46 R⊙) are in agreement with dynamical constraints and evolutionary tracks. Conclusions. In contrast to HD 142527, we find no evidence that a close-in stellar companion is responsible for the resolved disk features of HD 100453, HD 100546, HD 135344 B, and PDS 70. Instead of a dynamical effect by a stellar companion, the formation of giant planets or even low-mass brown dwarfs could be shaping the innermost environment (≲20 au) of these circumstellar disks, as is the case with the planetary system of PDS 70.

DeepRED Based Sparse SAR Imaging

The integration of deep neural networks into sparse synthetic aperture radar (SAR) imaging is explored to enhance SAR imaging performance and reduce the system’s sampling rate. However, the scarcity of training samples and mismatches between the training data and the SAR system pose significant challenges to the method’s further development. In this paper, we propose a novel SAR imaging approach based on deep image prior powered by RED (DeepRED), enabling unsupervised SAR imaging without the need for additional training data. Initially, DeepRED is introduced as the regularization technique within the sparse SAR imaging model. Subsequently, variable splitting and the alternating direction method of multipliers (ADMM) are employed to solve the imaging model, alternately updating the magnitude and phase of the SAR image. Additionally, the SAR echo simulation operator is utilized as an observation model to enhance computational efficiency. Through simulations and real data experiments, we demonstrate that our method maintains imaging quality and system downsampling rate on par with deep-neural-network-based sparse SAR imaging but without the requirement for training data.

Multiplexed terahertz multifocal metalens based on stretchable single-, dual-, and quad-axis synthetic rectangular optical sparse aperture

As ultrathin optical elements with subwavelength structures densely arranged, planar lens or called metalens can effectively focus incident wave. Due to the compact design and wide operating frequency of metalens, it becomes promising in applications like information encryption, imaging, and wireless communication fields. However, the fabrication of large-aperture metalens is still time-consuming and costly while the focusing performances are commonly tuned by introducing active materials requiring extra stimuli, such as laser and electrical pulses. In this study, five designs of metalenses with synthetic rectangular aperture are proposed and demonstrated with excellent focusing performances. By reconfiguring the synthetic aperture, focusing properties like focusing lengths, full width at half maximum values, and modulation transfer function can be tuned with great flexibility. With a polarization-dependent phase-shifting structure, the proposed metalens shows different focusing performances with incident wave under transverse magnetic field and transverse electric field modes. By utilizing spatial multiplexing, terahertz variable foci metalenses based on a stretchable single-, dual- and quad-axis synthetic rectangular optical sparse aperture are investigated to achieve tunable multifocal performances. These designs provide a novel strategy that can reconfigure the synthetic aperture with stretchable substrate to make metalens with multifunction and active tunability, which show promising applications in imaging, information encryption, and space telescope areas.

PIN: Sparse Aperture ISAR Imaging via Self-Supervised Learning

PIN: Sparse Aperture ISAR Imaging via Self-Supervised Learning

Imaging Characteristics of One-Dimensional Rotating Synthetic Array for Sparse Aperture

Imaging Characteristics of One-Dimensional Rotating Synthetic Array for Sparse Aperture

Piston sensing for sparse aperture systems via all-optical diffractive neural network

Piston sensing for sparse aperture systems via all-optical diffractive neural network

Fast Sparse Aperture ISAR Imaging for Maneuvering Target by CZT- and NCS-Based Approximated Observation Model

Fast Sparse Aperture ISAR Imaging for Maneuvering Target by CZT- and NCS-Based Approximated Observation Model

Laboratory multistatic 3D SAR with polarimetry and sparse aperture sampling

AbstractWith the advent of constellations of SAR satellites, and the possibility of swarms of SAR UAV's, there is increased interest in multistatic SAR image formation. This may provide advantages including allowing three‐dimensional image formation free of clutter overlay; the coherent combination of bistatic SAR geometries for improved image resolution; and the collection of additional scattering information, including polarimetric. The polarimetric collection may provide useful target information, such as its orientation, polarisability, or number of interactions with the radar signal; distributed receivers would be more likely to capture any bright specular responses from targets in the scene, making target outlines distinct. Highlight results from multistatic polarimetric SAR experiments at the Cranfield University GBSAR laboratory are presented, illustrating the utility of the approach for fully sampled 3D SAR image formation, and for sparse aperture SAR 3D point‐cloud generation with a newly developed volumetric multistatic interferometry algorithm.

Polarization Characteristics of Sparse Aperture Optical Imaging Systems

Polarization Characteristics of Sparse Aperture Optical Imaging Systems