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DOE OSTI2024

HWO Pupil Chopping Wavefront Sensing and Control FY24 Mini-Project Writeup

This document is a summary of work completed in June through July 2024 to assess the feasibility of implementing non-coronagraphic pupil chopping (PC) for Habitable Worlds Observatory (HWO) primary mirror segment stabilization with an on- and/or off-axis Natural Guide Star (NGS) or Laser Guide Star (LGS). This work considers three different PC configurations: (1) a non-common path (NCP) continuous low-order Deformable Mirror (DM), (2) using the telescope’s primary mirror (M1) segment(s), and (3) using a NCP low-order segmented DM. We show that option 2 likely needs a LGS for guiding, while options 1 and 3 may be able to guide on NGSs. We also separately evaluate spectral bandwidth limitations in the absence of photon noise, showing that the technique is limited to Δλ/λ ≲ 25% in a standard imaging configuration but Δλ/λ ≲ 150% when using a NCP Wynne corrector.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1998

Wavefront Sensing and Control for a Next Generation Space Telescope

The Next Generation Space Telescope will provide more than ten times the collecting area of the Hubble Space Telescope in a package that fits into the shroud of an expendable launch vehicle. This paper illustrates the operation and performance expected for initial telescope alignment, segment phasing, and fine figure control for the NGST yardstick design.

wavefront↗

NASA NTRS1980

Seasat data applications by commercial users

The Seasat Program was initiated as a proof-of-concept mission to evaluate the effectiveness of a study of remotely sensed oceanographic phenomena from a satellite platform. From its inception the Seasat Program has been user-oriented. The user comprised three major segments of the ocean community, including academic users, users within various government agencies, and commercial users. Commercial users represent a broad spectrum of commercial marine industries. The commercial users proposed to NASA to carry out a pilot demonstration to assess the utility of Seasat data in the private sector. The commercial users' program is considered along with case studies that utilize Seasat data, and aspects of real-time system operation. Attention is given to the potential value of an employment of synthetic aperture radar, the altimeter, the scatterometer, and the scanning multichannel microwave radiometer.

Montgomery, D. R.↗

NASA NTRS1992

Adaptive optics for array telescopes using piston-and-tilt wave-front sensing

A near-infrared adaptive optics system operating at about 50 Hz has been used to control phase errors adaptively between two mirrors of the Multiple Mirror Telescope by stabilizing the position of the interference fringe in the combined unresolved far-field image. The resultant integrated images have angular resolutions of better than 0.1 arcsec and fringe contrasts of more than 0.6. Measurements of wave-front tilt have confirmed the wavelength independence of image motion. These results show that interferometric sensing of phase errors, when combined with a system for sensing the wave-front tilt of the individual telescopes, will provide a means of achieving a stable diffraction-limited focus with segmented telescopes or arrays of telescopes.

Wizinowich, P.↗

NASA NTRS2006

Precision Linear Actuators for the Spherical Primary Optical Telescope Demonstration Mirror

The Spherical Primary Optical Telescope (SPOT) is an ongoing research effort at Goddard Space Flight Center developing wavefront sensing and control architectures for future space telescopes. The 03.5-m SPOT telescope primary mirror is comprise9 of six 0.86-m hexagonal mirror segments arranged in a single ring, with the central segment missing. The mirror segments are designed for laboratory use and are not lightweighted to reduce cost. Each primary mirror segment is actuated and has tip, tilt, and piston rigid-body motions. Additionally, the radius of curvature of each mirror segment may be varied mechanically. To provide these degrees of freedom, the SPOT mirror segment assembly requires linear actuators capable of <lO-nm resolution over a total stroke of 5 mm. These actuators must withstand high static loads as they must support the mirror segment, which has a mass of -100 kg. A stepper motor driving a differential satellite roller screw was designed to meet these demanding requirements. Initial testing showed that the actuator is capable of sub-micron repeatability over the entire 6-mm range, and was limited by 100-200 nm measurement noise levels present in the facility. Further testing must be accomplished in an isolated facility with a measurement noise floor of <5 nm. Such a facility should be ready for use at GSFC in the early summer of 2006, and will be used to better characterize this actuator.

Budinoff, Jason↗

NASA NTRS2010

Wavefront Control and Image Restoration with Less Computing

PseudoDiversity is a method of recovering the wavefront in a sparse- or segmented- aperture optical system typified by an interferometer or a telescope equipped with an adaptive primary mirror consisting of controllably slightly moveable segments. (PseudoDiversity should not be confused with a radio-antenna-arraying method called pseudodiversity.) As in the cases of other wavefront- recovery methods, the streams of wavefront data generated by means of PseudoDiversity are used as feedback signals for controlling electromechanical actuators of the various segments so as to correct wavefront errors and thereby, for example, obtain a clearer, steadier image of a distant object in the presence of atmospheric turbulence. There are numerous potential applications in astronomy, remote sensing from aircraft and spacecraft, targeting missiles, sighting military targets, and medical imaging (including microscopy) through such intervening media as cells or water. In comparison with prior wavefront-recovery methods used in adaptive optics, PseudoDiversity involves considerably simpler equipment and procedures and less computation. For PseudoDiversity, there is no need to install separate metrological equipment or to use any optomechanical components beyond those that are already parts of the optical system to which the method is applied. In Pseudo- Diversity, the actuators of a subset of the segments or subapertures are driven to make the segments dither in the piston, tilt, and tip degrees of freedom. Each aperture is dithered at a unique frequency at an amplitude of a half wavelength of light. During the dithering, images on the focal plane are detected and digitized at a rate of at least four samples per dither period. In the processing of the image samples, the use of different dither frequencies makes it possible to determine the separate effects of the various dithered segments or apertures. The digitized image-detector outputs are processed in the spatial-frequency (Fourier-transform) domain to obtain measures of the piston, tip, and tilt errors over each segment or subaperture. Once these measures are known, they are fed back to the actuators to correct the errors. In addition, measures of errors that remain after correction by use of the actuators are further utilized in an algorithm in which the image is phase-corrected in the spatial-frequency domain and then transformed back to the spatial domain at each time step and summed with the images from all previous time steps to obtain a final image having a greater signal-to-noise ratio (and, hence, a visual quality) higher than would otherwise be attainable.

Lyon, Richard G.↗

NASA NTRS1988

Report of the Asilomar 3 LDR Workshop

The conclusions and recommendations of the workshop held to study technology development issues critical to the Large Deployable Reflector (LDR) are summarized. LDR is to be a dedicated, orbiting, astronomical observatory, operating at wavelengths from 30 to 1000 microns, a spectral region where the Earth's atmosphere is almost completely opaque. Because it will have a large, segmented, passively cooled aperture, LDR addresses a wide range of technology areas. These include lightweight, low cost, structural composite reflector panels, primary support structures, wavefront sensing and adaptive optics, thermal background management, and integrated vibration and pointing control systems. The science objectives for LDR present instrument development challenges for coherent and direct arrayed detectors which can operate effectively at far infrared and submillimeter wavelengths, and for sub-Kelvin cryogenic systems.

Mahoney, M. J.↗

NASA NTRS1982

Can crop types be resolved using mixture distribution components - Some initial results and implications

For the analysis of remotely sensed data, it is frequently necessary to design a classifier in order to locate a ground cover class of interest or to estimate the proportion of this ground cover class. Advantages of a mixture distribution formulation are discussed, and a description is presented of the results of estimating the proportion of small grains in ten Landsat data segments using the mixture model. It is found that the mixture model proportion estimates have a very low variance and coefficient of variation. The discussed investigation implies that the mixtures model is a viable method for determining the distributions of classes of interest in remote sensing problems and in estimating the proportions of these classes directly.

Lennington, R. K.↗

DOE OSTI2024

Characterizing Mesoscale Cellular Convection in Marine Cold Air Outbreaks With a Machine Learning Approach

Abstract During marine cold‐air outbreaks (MCAOs), when cold polar air moves over warmer ocean, a well‐recognized cloud pattern develops, with open or closed mesoscale cellular convection (MCC) at larger fetch over open water. The Cold‐Air Outbreaks in the Marine Boundary Layer Experiment provided a comprehensive set of ground‐based in situ and remote sensing observations of MCAOs at a coastal location in northern Norway. MCAO periods that unambiguously exhibit open or closed MCC are determined. Individual cells observed with a profiling Ka‐band radar are identified using a watershed segmentation method. Using self‐organizing maps (SOMs), these cells are then objectively classified based on the variability in their vertical structure. The SOM nodes contain some information about the location of the cell transect relative to the center of the MCC. This adds classification noise, requiring numerous cell transects to isolate cell dynamical information. The SOM‐based classification shows that comparatively intense convection occurs only in open MCC. This convection undergoes an apparent lifecycle. Developing cells are associated with stronger updrafts, large spectrum width, larger amounts of liquid water, lower surface precipitation rates, and lower cloud tops than mature and weakening cells. The weakening of these cells is associated with the development of precipitation‐induced cold pools. The SOM classification also reveals less intense convection, with a similar lifecycle. More stratiform vertical cloud structures with weak vertical motions are common during closed MCC periods and are separated into precipitating and non‐precipitating stratiform cores. Convection is observed only occasionally in the closed MCC environment.

Meteorology & Atmospheric Sciences↗

NASA NTRS2008

Alignment-Insensitive Lower-Cost Telescope Architecture

This architecture features an active wavefront sensing and control scheme along with methods for measuring the relative positions of the primary to aft optics, such as the secondary mirror, and should enable larger and cheaper telescope architectures needed for future applications. A wavefront source/sensor is placed at the center of curvature of the primary mirror. The system provides continuous light onto a primary mirror that is retro-reflected onto itself. This allows the wavefront controller to constantly update the positions of the primary mirror segments (or deformable mirror actuators). Another function of this innovation involves using a concave mirror on the back of the secondary mirror (or other aft optic) that has the same center-of-curvature location (in defocus) as the primary mirror. The two return beams can be aligned next to each other on a detector, or radially on top of each other. This provides a means with which to measure the relative position of the primary to the secondary (or other aft optics), thus allowing for the removal of misalignment of the center-of-curvature source/sensor (meaning it doesn't need precision placement) and also provides a means with which to monitor the relative alignment over time. This innovation does not require extremely good thermal stability on the primary mirror and can thus be used in any thermal environment and with cheaper materials. In addition to this, the architecture lets one phase (or align) the primary mirror independent of whether a star or scene is in the field. The segmented, spherical primary allows for cost-effective three-meter class (e.g. Midex and Discovery) missions as well as enabling 30-meter telescope solutions that can be manufactured in a reasonable amount of time. The continuous wavefront sensing and control architecture enables missions for low-Earth-orbit.

Feinberg, Lee↗

NASA NTRS2010

Advanced Wavefront Sensing and Control Testbed (AWCT)

The Advanced Wavefront Sensing and Control Testbed (AWCT) is built as a versatile facility for developing and demonstrating, in hardware, the future technologies of wave front sensing and control algorithms for active optical systems. The testbed includes a source projector for a broadband point-source and a suite of extended scene targets, a dispersed fringe sensor, a Shack-Hartmann camera, and an imaging camera capable of phase retrieval wavefront sensing. The testbed also provides two easily accessible conjugated pupil planes which can accommodate the active optical devices such as fast steering mirror, deformable mirror, and segmented mirrors. In this paper, we describe the testbed optical design, testbed configurations and capabilities, as well as the initial results from the testbed hardware integrations and tests.

active optics↗

NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗

NASA NTRS2018

NASA NeMO-Net

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

NASA↗

NASA NTRS2018

Adaptive Load Control of Flexible Aircraft Wings Using Fiber Optic Sensing

Over the past century aircraft wing design has transformed from the morphing wing used on the Wright Flyer to rigid wings with little to no shape-tailoring abilities. Modern day wings are designed to fly at a single trim condition and optimized to have a maximum aerodynamic efficiency at only this condition. Shape morphing wings on the other hand have the potential to undergo geometric changes allowing them to adapt to their mission profiles. Several flight demonstrations have been conducted over the decades using morphing-wing technologies. Active wing-twist control was demonstrated on the X-53 Active Aeroelastic Wing (AAW) research project by utilizing multiple leading- and trailing-edge control surfaces. Passive morphing technology has been demonstrated on the Rockwell RPRV-870 Highly Maneuverable Aircraft Technology (HiMAT) aircraft. In the current study, the wings of a small unmanned aerial system (sUAS) were modified to have segmented control surfaces (SCS). The modifications include segmenting the original wing control surfaces (one flap and one aileron per wing) into 44 individual sections, each section having its own independent servo control motor. The wings were also instrumented with a network of over 1800 fiber-optic strain sensors (on four sensing fibers distributed over the top and bottom surfaces of the wing) monitoring the strain response of the wing to aerodynamic loading. The SCS positions were manipulated in real time to modify the spanwise lift distribution of the wings on the sUAS. The change in the structural response of the wings caused by load redistribution was quantified by measuring the bending strains on the upper and lower wing surfaces using an on-board compact fiber-optic strain sensing (cFOSS) system. A feedback controller was developed to control the SCS positions using strain-based shape estimations from the Displacement Transfer Function (DTF). Post-processing of the strain data allowed for the transverse displacement distributions and load distributions to be compared for the conventional and segmented control surface cases using displacements and loads algorithms developed by Richards and Ko at AFRC (refs. 5-10). While the current study focused on the shifting of the spanwise aerodynamic loads as quantified by displacements, future applications for loads or displacement control might include active gust alleviation and flutter suppression.

Pena, Francisco↗

NASA NTRS2001

SMOKE: Characterization of Smoke Particulate for Spacecraft Fire Detection

'Smoke' is a flight definition investigation whose purpose is to characterize the smoke particulate from microgravity smoke sources to enable improved design of future space-craft smoke detectors. In the earliest missions (Mercury, Gemini and Apollo), the crew quarters were so cramped that it was considered reasonable that the astronauts would rapidly detect any fire. The Skylab module, however, included approximately 30 UV-sensing fire detectors. The Space Shuttle Orbiter has nine particle-ionization smoke detectors in the mid-deck and flight deck. The detectors for the US segments of the International Space Station (ISS) are laser-diode, forward-scattering, smoke detectors. Current plans for the ISS call for two detectors in the open area of the module, and detectors in racks that have cooling air-flow. Due to the complete absence of microgravity data, all three of these detector systems were designed based upon 1-g test data and experience. As planned mission durations and complexity increase and the volume of spacecraft increases, the need for and importance of effective, crew-independent, fire detection will grow significantly, necessitating more research into microgravity fire phenomena. In 1997 the Comparative Soot Diagnostics Experiment (CSD) flew in the Orbiter Middeck as a Glovebox payload. The CSD experiment was designed to produce small quantities of smoke from several sources to obtain particulate samples and to determine the response of the ISS and Orbiter smoke detectors to these sources. Marked differences in the performance of the detectors compared to their behavior in 1-g were observed. In extreme cases, the detector used in the orbiter was completely blind to easily visible smoke from sources that were readily detected in 1-g. It is hypothesized but as yet unverified that this performance difference was due to enhanced growth of liquid smoke droplets in low-g. These CSD results clearly demonstrate that spacecraft smoke detector design cannot be based on 1-g experience.

Urban, D. L.↗

NASA NTRS2005

Multiple Layer Identification Label Using Stacked Identification Symbols

An automatic identification system and method are provided which employ a machine readable multiple layer label. The label has a plurality of machine readable marking layers stacked one upon another. Each of the marking layers encodes an identification symbol detectable using one or more sensing technologies. The various marking layers may comprise the same marking material or each marking layer may comprise a different medium having characteristics detectable by a different sensing technology. These sensing technologies include x-ray, radar, capacitance, thermal, magnetic and ultrasonic. A complete symbol may be encoded within each marking layer or a symbol may be segmented into fragments which are then divided within a single marking layer or encoded across multiple marking layers.

Harry F Schramm↗

NASA NTRS2005

Multiple layer identification label using stacked identification symbols

Schramm, Harry F.↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗