Distributed Aperture Radar Tomographic Sensors (DARTS) to Map Surface Topography and Vegetation Structure
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Chung, Soon-Jo↗
Engineering topics
Matsuka, Kai
Publications and source records attributed to Matsuka, Kai.
Distributed Aperture Radar Tomographic Sensors (darts) to Map Surface Topography and Vegetation Structure
Distributed Aperture Radar Tomographic Sensors (DARTS)is a mission concept being studied at the NASA Jet PropulsionLaboratory in collaboration with the California Institute ofTechnology to enable global and repeated imaging of surfacetopography and three-dimensional vegetation structure usingsingle-pass tomographic SAR technique. The observing systemconsists of a distributed formation of multiple small syntheticaperture radar platforms deployed in space with variabledistances to achieve look angle diversity and sensitivityto the vertical distribution of vegetation components. Ourgoal is to identify the optimal system configuration startingfrom documented community needs and mature the criticaltechnologies that lead to a viable implementation of DARTS.Here, we provide an overview of DARTS and describe ourapproach for designing and demonstrating single-pass SARtomographic systems as part of an on-going funded NASA Instrument Incubator Program effort.
Chung, Soon-Jo↗
Collaborative Pose Estimation of An Unknown Target Using Multiple Spacecraft
A reliable method for pose estimation of an unknown and uncooperative space target using monocular vision remains an open problem. Vision-based pose determination can be challenging in case of unfavorable illumination, time-varying conditions due to rotational motion and relative orbit, and scale ambiguity resolution. To address these challenges, we propose a novel collaborative pose determination algorithm called Multi- Spacecraft Simultaneous Estimation of Pose and Shape algorithm or M-SEPS.Within M-SEPS, a team of chaser spacecraft, each equipped with a monocular camera, exchange information over a local network to jointly estimate the relative kinematic state of the target and its sparse shape landmarks. In this approach, each spacecraft processes its own images and observes particular target landmarks in parallel and in a distributed fashion. Then, the local network is exploited by the spacecraft to share their consensus proposals and aggregate them to achieve the joint estimate. We validate our algorithm using simulations of relative orbits and observations, captured by each chaser spacecraft. To the best of the authors’ knowledge, this is the first cooperative, vision-based algorithm for estimating the pose and shape of a space object for an arbitrary number of spacecraft.
Chung, Soon-Jo↗