NISAR L-band digital electronics subsystem: A multichannel system with distributed processors for digital beam forming and mode dependent filtering

Chuang, Chung-Lun; Shaffer, S.; Niamsuwan, N.; Li, Samuel; Liao, Eric C.; Lim, Chester; Dương, Vũ; Volain, Barry; Vines, Ken; Yang, Muh-Wang; Wheeler, K.

doi:10.1109/radar.2016.7485225

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Such high-performance computing with FPGAs in space is being used in the near future for other low-risk NASA missions such as the NISAR synthetic aperture radar mission. 45…”

Section: Unknown Radiation Susceptibilitymentioning

confidence: 99%

Strategies for high order wavefront sensing and control (HOWFSC) computation on future space telescopes

Belsten

Pogorelyuk

Haughwout

et al. 2021

Techniques and Instrumentation for Detection of Exoplanets X

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Future planned space telescopes such as HabEx and LUVOIR will be used to directly image exo-Earths. These telescopes use coronagraph instruments to suppress starlight and resolve dim exoplanets. They will employ high order wavefront sensing and control (HOWFSC) to correct static and slow wavefront errors in the image plane to achieve contrasts above 10 9 . This work evaluates architectures to meet the computational requirements for HOWFSC algorithms with available processors.We find that the computational requirements of HOWFSC will impose unprecedented requirements on space-based components and that typical combinations of computational resource and control architecture will consume significant observation time. Science yield from the space telescope can be improved, and mission risk and cost reduced, by using co-flying or ground-in-the loop computational offload architectures.In particular, a high-capability co-flying processor could use commercial components 10 4 times more powerful than typical radiation hardened options. This would enable key HOWFSC algorithms to run in seconds rather than hours or days, removing operational constraints on the science mission. While commercial processors may be more susceptible to total ionizing dose radiation effects over the expected mission lifetime of 5-10 years, the relatively low cost of development and replacement launches make these co-flying processors an attractive option.We evaluate three major co-flying architecture trades: (i) inter-spacecraft distance, (ii) risk classification, and (iii) processor selection. We find that one or more low-cost replaceable co-flying processors with COTS components and flying several kilometers from the telescope spacecraft can provide all needed computation.

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“…Such high-performance computing with FPGAs in space is being used in the near future for other low-risk NASA missions such as the NISAR synthetic aperture radar mission. 45…”

Section: Unknown Radiation Susceptibilitymentioning

confidence: 99%

Strategies for high order wavefront sensing and control (HOWFSC) computation on future space telescopes

Belsten

Pogorelyuk

Haughwout

et al. 2021

Techniques and Instrumentation for Detection of Exoplanets X

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“…NISAR will be instrumented with multipolarimetric, dual-frequency L (24 cm wavelength) and S (10 cm wavelengths) band SARs for imaging the Earth. Notably, NISAR is equipped to receive 12 independent channels, enabling a 12-day global revisit cycle (Chuang et al, 2016). However, while the L-band SAR has the ability to collect all data while over land, the duty cycle of the S-band SAR is limited, and will be restricted to a planned subset of the Earth's surface.…”

mentioning

confidence: 99%

Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms

Adeli

Salehi

Mahidanpari

et al. 2021

Earth and Space Science

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Given the key role wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data coupled with advanced machine learning techniques have provided an unprecedented opportunity for mapping complex wetlands’ ecosystems. A recent partnership between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO) resulted in the design of the NASA‐ISRO SAR (NISAR) mission. In this study, the capability of L‐band simulated NISAR data for wetland mapping in Yucatan Lake, Louisiana, is investigated using two object‐based machine learning approaches: Support vector machine (SVM) and random forest (RF). L‐band Unmanned Aerial Vehicle SAR (UAVSAR) data are exploited as a proxy for NISAR data. Specifically, we evaluated the synergistic use of different polarimetric features for efficient delineation of wetland types, extracting 84 polarimetric features from more than 10 polarimetric decompositions. High spatial resolution National Agriculture Imagery Program imagery is applied for image segmentation using the mean‐shift algorithm. Overall accuracies of 74.33% and 81.93% obtained by SVM and RF, respectively, demonstrate the great possibility of L‐band prototype NISAR data for wetland mapping and monitoring. In addition, variable importance analysis using the Gini index for RF classifier suggests that H/A/ALPHA, Freeman‐Durden, and Aghababaee features have the highest contribution to the overall accuracy.

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The NASA-ISRO SAR (NISAR) mission dual-band radar instrument preliminary design

Rosen

Hensley

Shaffer

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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NISAR L-band digital electronics subsystem: A multichannel system with distributed processors for digital beam forming and mode dependent filtering

Cited by 5 publications

References 9 publications

Strategies for high order wavefront sensing and control (HOWFSC) computation on future space telescopes

Strategies for high order wavefront sensing and control (HOWFSC) computation on future space telescopes

Moving Toward L‐Band NASA‐ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object‐Based Classification of Wetlands Using Two Machine Learning Algorithms

The NASA-ISRO SAR (NISAR) mission dual-band radar instrument preliminary design

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