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This slide presentation reviews the importance of NASA's Deep Space Network (DSN) to space exploration, and future planned improvements to the communication capabilities that the network allows, in terms of precision, and communication power.
This slide presentation reviews some of the challenges and accomplishments of implementing a new program. The purpose of the presentation is to: (1) Share the challenges that were encountered formulating a new program concurrent with formulating & implementing new spacecraft development projects: (a) Immature mission concepts put on the fast track (b) Need to reconcile ambitious objectives with cost and budget reality (c) Changes of major stakeholders (d) Timing, timing, timing (e) Changing ground rules, assumptions, and risk tolerance (f) The role of centers, (2) Share the successes to date despite the challenges (3) Demonstrate how interdependencies between the program, projects, NASA HQ environment, and external political forces affect the process, and how expectations must be managed while dealing with external factors and great change.
Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors
The spatiotemporal evolution of marine heatwaves (MHWs) is explored using a tracking algorithm termed Ocetrac that provides objective characterization of MHW spatiotemporal evolution. Candidate MHW grid points are defined in detrended gridded sea temperature data using a seasonally varying temperature threshold. Identified MHW points are collected into spatially distinct objects using edge detection with weak sensitivity to edge detection and size threshold criteria. These MHW objects are followed in space and time while allowing objects to split and merge. Ocetrac is applied to monthly satellite sea surface temperature data from September 1981 through January 2021. The resulting MHWs are characterized by their intensity, duration, and total area covered. The global analysis shows that MHWs in the Gulf of Maine and Mediterranean Sea evolve within a relatively small region, while major MHWs in the Pacific and Indian Oceans are linked in space and time. The largest and most long lasting MHW using this method lasts for 60 months from November 2013 to October 2018, encompassing previously identified MHW events including those in the Northeast Pacific (2014-2015), the Tasman Sea (2015-2016, 2017-2018), and the Great Barrier Reef (2016).
The growing number of large-scale constellations and the improvements made in the tracking and detection of resident space objects have contributed to the increase of satellite conjunction events. As a result of the increase of objects in Earth orbit, the timeline for risk analysis computations for satellite collision avoidance is more urgent when conjunctions occur. It is imperative NASA maintains a space environment safety functionality for the agency that allows CARA remote access to the ground system while maintaining redundancy. CARA’s On-premises (On-prem) ground system would no longer be able to meet these access and redundancy requirements, hence the investigation and eventual implementation of a Cloud-based ground station infrastructure as a viable solution. This paper will provide an overview of the principal parts of the Conjunction Assessment (CA) risk analysis process used at CARA, summarize the key drivers towards the Cloud solution, and share key lessons learned that are significant for any operational entity considering a cloud solution for real-time satellite operations to ensure a sustainable space traffic environment for all.
I. During each nine-hour shift (or upon request), the Orbital Safety Analyst (OSA) at JSpOC updates the entire tracked catalog with the latest tracking data from the SSN and screens this catalog against NASA s assets. a) For ISS operations, a 72-hour advance screening is performed. b) For Shuttle orbit operations, a 36-hour advance screening is performed. c) If a vehicle is performing a maneuver during the screening period, OSA uses post-reboost-trajectory data supplied by Mission Control Center-Houston (MCC-H) for screening. II. An automated process is used to detect any conjunctions within 10 x 40 x 40 km box (centered on the vehicle) using Special Perturbation (SP) processing: a) Box dimensions are Radial x Downtrack x Crosstrack. b) Any object found within this box has the tracking tasking level increased to improve (hopefully) its uncertainty in its current and predicted orbital trajectory. c) OSA informs NASA if any object is found inside a 2 x 25 x 25 km box then creates and sends an Orbital Conjunction Message (OCM) to NASA which contains detailed information about the conjunction. d) For Shuttle, the box size used for screening and reporting is altered for special operations (day of rendezvous, launch screening, etc.).
Experiments demonstrated feasibility of synthetic-discriminant-function filter encoded in binary spatial light modulator. Filter is part of optoelectronic apparatus indicating degree of correlation between input image and any number of training images of same object at various positions and orientations. Used in optical correlator to recognize rotated images, or in tracking system to recognize moving vehicle or other substantially rigid object from any direction.
This research was initiated as a part of the effort at the NASA Ames Research Center to design a computer vision based system that can enhance the safety of navigation by aiding the pilots in detecting various obstacles on the runway during critical section of the flight such as a landing maneuver. The primary goal is the development of algorithms for detection of moving objects from a sequence of images obtained from an on-board video camera. Image regions corresponding to the independently moving objects are segmented from the background by applying constraint filtering on the optical flow computed from the initial few frames of the sequence. These detected regions are tracked over subsequent frames using a model based tracking algorithm. Position and velocity of the moving objects in the world coordinate is estimated using an extended Kalman filter. The algorithms are tested using the NASA line image sequence with six static trucks and a simulated moving truck and experimental results are described. Various limitations of the currently implemented version of the above algorithm are identified and possible solutions to build a practical working system are investigated.
The lunar-probe tracking program at the Jet Propulsion Laboratory has two prime objectives: (1) provide real-time predictions of the direction of the probe from various observation stations; (2) establish a reliable trajectory corresponding to the actual flight path of the probe. The tracking program, although developed for use with lunar probes, can be used for interplanetary probes if certain modifications are made. The program, as developed for the IBM 704 digital computer, has two distinct phases. First, the equations of motion and the variational equations are integrated to each observation time where the elements of the equation A (sub u) equals b [linearization of the maximum likelihood equations] are computed. The second phase is concerned with the solution of a specified subset of A (sub u) equals b. Flexibility and ease of operation have been major objectives in writing the 704 program. The number of data points and tracking stations that may be used is limited only by computing time and core storage. Input formats and operating instructions are presented for utilizing the various computational options available in the program.
The results of self-consistent photoionization modeling of 38 Magellanic Cloud PNe are presented and used to construct an H-R diagram for the central stars and to obtain both the nebular chemical abundances and the physical parameters of the nebulae. T(eff)s derived from nebular excitation analysis are in agreement with temperatures derived by the classical Zanstra method. There is a linear correlation between log T(eff) and the excitation class. The majority of the central stars in the sample with optically thick nebulae have masses between 0.55 and 0.7 solar mass and are observed during their hydrogen-burning excursion toward high temperatures. Optically thin objects are found scattered throughout the H-R diagram, but tend to have a somewhat smaller mean mass. Type I PN are found to have high core masses and to lie on the descending branch of the evolutionary tracks. The nebular mass of the optically thick objects is closely related to the nebular radius, and PN with nebular masses over one solar are observed.
In the past 40 years, thousands of objects have been placed in Earth orbit and are being tracked. Space hardware reentry survivability must be evaluated to assess risks to human life and property on the ground. The objective of this paper is to present results of a study to determine altitude of demise (burn-up) or survivability of reentering objects. Two NASA/JSC computer codes - Object Reentry Survival Analysis Tool (ORSAT) and Miniature ORSAT (MORSAT) were used to determine trajectories, aerodynamic aerothermal environment, and thermal response of selected spacecraft components. The methodology of the two codes is presented, along with results of a parametric study of reentering objects modeled as spheres and cylinders. Parameters varied included mass, diameter, wall thickness, ballistic coefficient, length, type of material, and mode of tumbling/spinning. Two fragments of a spent Delta second stage undergoing orbital decay, stainless steel cylindrical propellant tank and titanium pressurization sphere, were evaluated with ORSAT and found to survive entry, as did the actual objects. Also, orbital decay reentry predictions of the Japanese Advanced Earth Observing Satellite (ADEOS) aluminum and nickel box-type components and the Russian COSMOS 954 satellite beryllium cylinders were made with MORSAT. These objects were also shown to survive reentry.
A laboratory study was conducted to investigate the effect of spurious simulator yaw motions on a pilot's control performance. A second objective was to compare the efficiency of static and dynamic simulator tracking in previously unexamined vehicle dynamics. Twelve airline pilots served as subjects in a moving-base flight simulator under congruent-motion, spurious-motion, and no-motion conditions. The results indicated a significant increase in the amount of error with increasing levels of spurious motion during the initially administered series of trials. The influence of spurious motion, however, was absent in a second series of trials. The data suggest that the pilots learned to compensate in their performance for the spurious inputs. It was also found that congruent visual and rotational cueing produced superior performance to that of tracking with visual information alone.
In exploration-oriented robotic missions for disaster relief in unknown subterranean environments, it is of prime importance for a human supervisor to rapidly gain situational awareness of salient objects within the environment. In this paper we present an automated object detection pipeline that is adaptable to heterogeneous robots with arbitrary sensor configurations. It has been deployed in time-critical scenarios with multiple collaborative robots in a variety of demanding underground environments. For visually observable objects, detections are made in both the visible and thermal spectra using a state-of-the-art machine learning framework for object detection and classification. Our pipeline can be rapidly adapted to a specific task by using a small, structured dataset to fine-tune a pre-trained convolutional neural network (CNN). Relative localization is separated from the CNN for speed of operation. A robust architecture for localization is used with outlier rejection and a hierarchy of fall-back distance measurement methods. Point-source objects such as gas and WiFi hotspots can also be detected, by tracking signal strength over time and presenting an intuitive visualization on a map. Observations of each object types are presented to the operator in ranked confidence order for final evaluation.
The space station currently under consideration by NASA must simultaneously track and communicate with the many vehicles and objects surrounding it in orbit. While the Space Shuttle has 23 antennas, more than 50 will be required by the space station. In addition to Shuttle-compatible equipment at P, L, C, S, and Ku bands, the station system will probably incorporate Ka, W, IR and optical frequency equipment for tracking and communications. A major design challenge is foreseen in the placement of separate antennas, lenses and reflectors over the station's external geometry in order to give both the overlapping fields of view required for spherical coverage and the radiation of unambiguous navigation guide beams and markers. Adaptive distributed element arrays are under consideration. Another approach to spherical coverage involves the use of omnidirectional antennas which both transmit and receive RF energy over a wide range of angles.
The Applications Technology Satellite (ATS) program has involved geosynchronous spacecraft technology made possible by the increased capability of the Atlas-Agena launch vehicle. The ATS-1-5 program developed spin stabilized and gravity gradient stabilized spacecraft for geosynchronous orbit, earth-oriented antennae for increased antenna gain, earth-scanning meteorological experiments for continuous weather monitoring, and made possible satellite communications to mobile terminals. The ATS-6 program developed a space-deployed, high-gain antenna for color television broadcast to low-cost ground terminals and precise spacecraft attitude control for pointing, slewing, and tracking applications. All of the ATS program objectives were successfully met, except for the gravity gradient experiments. ATS-1, -3, -5, and -6 are in orbit and operational.
The configuration of the Voyager spacecraft is described as well as the subsystems for power, temperature control, attitude control, and propulsion. Major features of Jupiter and Saturn including their atmospheres, surfaces, and natural satellites are discussed. The 13 onboard experiments and their scientific objectives are explained. Other aspects covered include tracking, data acquisition, and the mission control and computing center. Members of the Voyager team and subcontractors are listed.
In addition to the advantages generally associated with orbital platforms, such as improved reliability, economies of scale, simple connectivity of elements, reduced tracking demands and the restraint of orbital object population growth, geostationary platforms yield: (1) continuous access by fixed ground antennas for communications services; (2) continuous monitoring of phenomena over chosen regions of the earth's surface; (3) a preferred location for many solar-terrestrial physics experiments. The geostationary platform also offers a low-risk and economical solution to the impending saturation of the orbital arc/frequency spectrum, maximizing the capacity of individual slots and increasing the utility of the entire arc. It also allows the use of many small, simple and inexpensive earth stations through complexity inversion and high power per beam. Block diagram and operational flowcharts are provided.
The orbital debris environment of objects larger than 1 sq met. in low Earth orbit (LEO) is fairly well defined. Objects of this size and altitude are tracked by NORAD with sufficient accuracy that collisions can be avoided by maneuvering a spacecraft away from regions of close approach. However for sizes smaller and at the higher geosynchrnous altitudes, the environment is not so well defined, and must be approached statistically. In order to determine if a significant number of these particles exist, or can be realistically produced, to cause a problem, a combination of modeling and data gathering is required.