Although no known asteroid poses a threat to Earth for at least the next century, the catalogue of near-Earth asteroids is incomplete for objects whose impacts would produce regional devastation1,2. Several approaches have been proposed to potentially prevent an asteroid impact with Earth by deflecting or disrupting an asteroid1–3. A test of kinetic impact technology was identified as the highest-priority space mission related to asteroid mitigation1. NASA’s Double Asteroid Redirection Test (DART) mission is a full-scale test of kinetic impact technology. The mission’s target asteroid was Dimorphos, the secondary member of the S-type binary near-Earth asteroid (65803) Didymos. This binary asteroid system was chosen to enable ground-based telescopes to quantify the asteroid deflection caused by the impact of the DART spacecraft4. Although past missions have utilized impactors to investigate the properties of small bodies5,6, those earlier missions were not intended to deflect their targets and did not achieve measurable deflections. Here we report the DART spacecraft’s autonomous kinetic impact into Dimorphos and reconstruct the impact event, including the timeline leading to impact, the location and nature of the DART impact site, and the size and shape of Dimorphos. The successful impact of the DART spacecraft with Dimorphos and the resulting change in the orbit of Dimorphos7 demonstrates that kinetic impactor technology is a viable technique to potentially defend Earth if necessary.
This work presents a method with which to automate simple aspects of geologic image analysis during space exploration. Automated image analysis on board the spacecraft can make operations more efficient by generating compressed maps of long traverses for summary downlink. It can also enable immediate automatic responses to science targets of opportunity, improving the quality of targeted measurements collected with each command cycle. In addition, automated analyses on Earth can process large image catalogs, such as the growing database of Mars surface images, permitting more timely and quantitative summaries that inform tactical mission operations. We present TextureCam, a new instrument that incorporates real-time image analysis to produce texture-sensitive classifications of geologic surfaces in mesoscale scenes. A series of tests at the Cima Volcanic Field in the Mojave Desert, California, demonstrated mesoscale surficial mapping at two distinct sites of geologic interest.
[1] Science missions have limited lifetimes, necessitating an efficient investigation of the field site. The efficiency of onboard cameras, critical for planning, is limited by the need to downlink images to Earth for every decision. Recent advances have enabled rovers to take follow-up actions without waiting hours or days for new instructions. We propose using built-in processing by the instrument itself for adaptive data collection, faster reconnaissance, and increased mission science yield. We have developed a machine learning pixel classifier that is sensitive to texture differences in surface materials, enabling more sophisticated onboard classification than was previously possible. This classifier can be implemented in a Field Programmable Gate Array (FPGA) for maximal efficiency and minimal impact on the rest of the system's functions. In this paper, we report on initial results from applying the texturesensitive classifier to three example analysis tasks using data from the Mars Exploration Rovers. Lett., 40,[4188][4189][4190][4191][4192][4193]
The Earth Sciences Decadal Survey identifies a multiangle, multispectral, high-accuracy polarization imager as one requirement for the Aerosol-Cloud-Ecosystem (ACE) mission. JPL has been developing a Multiangle SpectroPolarimetric Imager (MSPI) as a candidate to fill this need. A key technology development needed for MSPI is on-board signal processing to calculate polarimetry data as imaged by each of the 9 cameras forming the instrument. With funding from NASA's Advanced Information Systems Technology (AIST) Program, JPL is solving the real-time data processing requirements to demonstrate, for the first time, how signal data at 95 Mbytes/sec over 16-channels for each of the 9 multiangle cameras in the spaceborne instrument can be reduced on-board to 0.45 Mbytes/sec. This will produce the intensity and polarization data needed to characterize aerosol and cloud microphysical properties. Using the Xilinx Virtex-5 FPGA platform, a polarimetric processing least-squares fitting algorithm is under development to meet MSPI's on-board processing (OBP) requirements. The Virtex-5 FPGA is not yet space-flight qualified; however, an in-flight validation of this technology on a pre-cursor CubeSat mission is valuable toward advancing the technology readiness level for MSPI and the ACE mission.
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