Traditional data collects of high priority targets require immense planning and resources. When novel operating conditions (OCs) or imaging parameters need to be explored, typically synthetic simulations are leveraged. While synthetic data can be used to assess automatic target recognitions (ATR) algorithms; some simulation environments may inaccurately represent sensor phenomenology. To levitate this issue, a scale model approach is utilized to provide accurate data in a laboratory setting. This work demonstrates the effectiveness of a resource cognizant approach for collecting IR imagery suitable to assessing ATR algorithms. A target of is interest is 3D printed at 1/60th scale with a commercial printer and readily available materials. The printed models are imaged with a commercially available IR camera in a simple laboratory setup. The collected imagery is used to test ATR algorithms when trained on a standard IR ATR dataset; the publicly available ARL Comanche FLIR dataset. The performance of the selected ATR algorithms when given sampled of scale model data is compared to the performance of the same algorithms when using the provided measured data.
We present an exploration of collection geometries for producing three-dimensionally (3D) focused synthetic aperture radar (SAR) derived point clouds. We consider collection geometries that can be produced by a series of continuous curves such as multiple flight paths of a fixed wing aircraft or multiple passes of a satellite orbiting the earth. As part of our analysis, we use sparse methods to reconstruct undersampled radar data. We use back-projection to focus the radar data into the spatial domain, onto a uniform volumetric grid. Additionally, we use a 3D resonance finding algorithm to extract scattering centers from volumetric radar data to produce 3D point clouds. Our analysis is based upon synthetic radar data produced using the parameters derived from our laboratory’s in-door turntable inverse synthetic radar aperture (ISAR) system. A key point of our analysis is to determine how many repeat passes are required to achieve a given fidelity of an object’s 3D representation. Analysis will include a comparison with interferometric methods, particularly with regard to the fidelity and the point cloud density. We use a digital model of a civilian pickup truck that has been validated for use in synthetic prediction, both as a full-size model in outdoor collects as well as a reduced scale model measured indoors in our lab. Future research directions are also discussed.
Commonly, data exploitation for single sensors utilizes two-dimensional (2D) imagery. To best combine information from multiple sensing modalities, each with their own fundamental differences, we utilize sensor fusion to capture and leverage the inherent weaknesses from different sensing modalities. When fusing multiple sensor modalities together, this approach quickly becomes intractable as each sensor has unique projection planes and resolution. In this work, we present and analyze a data-driven approach for fusing multiple modalities by extracting data representations for each sensor into three-dimensional (3D) space, supporting sensor fusion natively in a common frame of reference. Photogrammetry and computer vision methods for recovering point clouds, such as structure from motion and multi-view stereo, from 2D electro-optical imagery has shown promising results. Additionally, 3D data representations can also be derived from interferometric synthetic aperture radar (IFSAR) and lidar sensors. We use point cloud representations for all three modalities, which allow us to leverage each sensing modality’s individual strengths and weaknesses. Given our data-driven focus, we emphasize fusing the point cloud data in controlled scenarios with known parameters. We also conduct an error analysis for each sensor modality based upon sensor position, resolution, and noise.
We present an analysis of image reconstruction quality that includes the use of traditional and deep-learning quality metrics for sparse reconstructions of three-dimensionally (3D) focused synthetic aperture radar (SAR) data. A major goal of our analysis is to explore the usefulness of various metrics to demonstrate their utility in 3D focused scenarios. We make use of synthetic prediction to help fully span the large parameter space of a two-dimensional cross-range aperture. The analysis including the synthetic prediction will help guide future measurements of scale models in our compact radar range.1
We present experiments to explore the use of deep neural network classification models for estimating the orientation of objects with linear structures from polarimetric radar data. We derive all radar data from two physical model aircraft and their corresponding computerized surface models. We make extensive use of synthetic pre- diction to help fully span the large parameter space as is consistent with best practice. Synthetic predictions are based upon a linear quad-polarized (H: horizontal, V: vertical) Ka-band stepped frequency measurement inverse synthetic aperture radar (ISAR) turntable system located inside the Air Force Research Laboratory (AFRL) Sensor Directorate's Indoor Range. The use of multiple polarimetric channels in a deep learning classification framework are shown to significantly help estimate orientation when the co-polarization channels significantly differ from each other. Future research directions are discussed.
We evaluate a recently reported algorithm for computing frequency-dependent radar imagery in scenarios relevant for performing spectral feature identification. For each image pixel in the spatial domain a computed frequency dependent reflectivity is used to produce a corresponding spectral feature identification. We show that this novel image reconstruction technique is capable of considerable flexibility for achieving fine spectral resolution in comparison with previous techniques based on conventional synthetic aperture radar (SAR), yet new challenges are introduced with regard to achieving fine range resolution.
The coupling length of the dielectric optical waveguide is inversely proportional to the difference of the even- and odd-mode propagation constants. It is important to accurately determine these values, since their difference is in a fraction of 10-3. To resolve this small difference, simulation using 2-dimensional finite-difference in time-domain method requires long computation time. Moreover, the time-domain method is not efficient to determine the modal functions. Frequency-domain method is cast as an eigenvalue solver, and the modal functions can be directly solved with the corresponding propagation constant. However, this method requires an initial knowledge of the eigenvalue to efficiently determine the modes of interest. This initial knowledge can be provided from the time-domain method. In this study, we conduct analysis of an AlGaN/GaN dielectric optical waveguide coupler combining time- and frequency-domain methods. Taking advantage from both methods, the propagation constants and the modal functions were obtained with a reasonable computation time.
We present a theoretical analysis of the optical physics of tapered oxide apertures in long- and short-cavity VCSELs. We apply our quasi-exact vector finite element model to a USC (long cavity) and U. Texas (short cavity) VCSEL to compute the electric field distribution, transverse confinement factor, diffraction rate, and threshold gain of the fundamental lasing mode. Making qualitative reference to the Hegblom, et al model, we analyze our results to deduce the fundamental physical effects of the tapered oxide aperture. We find that tapered oxides reduce diffraction loss through two separate physical phenomena: (1) a reduction in transverse confinement yielding a flatter phase front, and (2) an effective lens which acts to refocus the naturally diffracting wave front. We further find that in most VCSELs an inherent trade-off exists between minimizing the diffraction loss and maximizing the optical mode-to-gain interaction. To achieve the ultimate goal of (near) thresholdless lasing, this trade-off must be overcome: diffraction loss must be eliminated while simultaneously minimizing the mode volume. We conclude with a suggestion for a novel cavity design, which in theory achieves this goal.
We simulate the current-voltage characteristics of an InGaAs/AlAs resonant-tunneling diode under dark and illuminated conditions. The current is given by a tunneling formula that has been generalized to allow for quantum mechanical effects in the contacts. The optically generated carriers effect on the current-voltage characteristic is included through the use of a rate equation. This method of determining the optical response is shown to be accurate at low intensity and useful for extracting the recombination lifetime. The existing simulator shows great promise as a design tool for optical RTDs and related devices.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.