Smap radiometer rfi study status review
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SMAP Radiometer RFI Study Status Review. Overview. RFI in the SMAP L-band Radiometer is a major concern, being examined by the SMAP radiometer team (J. Piepmeier, NASA GSFC, study lead) Goal of this telecon: Peer review of analysis process and datasets

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SMAP Radiometer RFI Study Status Review

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Smap radiometer rfi study status review

SMAP Radiometer RFI StudyStatus Review

SMAP RFI Review Telecon


Overview

Overview

  • RFI in the SMAP L-band Radiometer is a major concern, being examined by the SMAP radiometer team

    • (J. Piepmeier, NASA GSFC, study lead)

  • Goal of this telecon: Peer review of analysis process and datasets

    • is analysis reasonable for forecasting SMAP RFI environment?

    • anything missing?

    • is the methodology reasonable?

  • Desired outcome:

    • Feedback from participants during telecon or via email within 24 hours

    • Progress toward consensus on any recommendations for SMAP

  • Invited Participants:

    • SMAP SDT (Njoku, O’Neill, Jackson, Johnson, Moghaddam, Tsang, Entekhabi, McDonald)

    • Instrument team (Piepmeier, Hudson, Medeiros, Spencer)

    • SMAP RFI WG

      (Gasiewski, Camps, Laymon, Ruf, LeVine, DeRoo, Li, Yueh, Dinardo, Skou)


  • Outline

    Outline

    • SMAP overview + RFI study roadmap and previous forecasts

    • Results from SMAPVEX08 campaign

      • Campaign and hardware description

      • RFI examples

      • Mapping airborne results to SMAP

      • Overall RFI statistics

    • Updated forecasts

      • Second/Third harmonics of TV broadcasts

      • Second harmonics of new 700 MHz cellphone allocation

    • RFI impact on SMAP error budget

    • Discussion


    Smap overview

    SMAP Overview

    • Soil Moisture Active Passive (SMAP) mission designed to measure surface soil moisture and freeze/thaw state

      • Soil moisture accuracy 4% volumetric, 10 km/3-day resolutions

      • Excludes vegetation having VWC> 5 kg/m2

      • Binary freeze/thaw transitions >45N latitude

        • 80% classification accuracy, 3 km /2-day resolutions

    • Previous HYDROS efforts provide baseline design

      • L-band Radar and Radiometer, radiometer measures H/V/U

      • Conically scanned footprint, 40 deg. inc angle, ~ 1000 km swath

        • radiometer spatial resolution ~ 40 km

      • Approximately 15 msec integration time/ footprint + fore/aft looks

      • 15 msec NEDT estimated 0.95 K, 0.67 after fore/aft combination

      • Analog baseline sub-samples 15 msec into 64 x 240 usec intervals

        • No frequency resolution


    Study roadmap and previous forecasts

    Study roadmap and previous forecasts

    • An RFI study roadmap has been developed for SMAP

    • Study goals:

      • Characterize RFI threat using forecasts and measured datasets

        • SMAPVEX08 provides an important dataset

      • Model RFI detection/mitigation performance of possible hardware modifications

      • Final outcome: recommendations for radiometer design

    • An RFI forecast study was performed previously for HYDROS

      • Investigated impact of US radar systems on HYDROS

      • Impact of “tails” of radar emissions into SMAP bandwidth/filter sidebands

    • Similar analyses also performed for SMOS


    Study roadmap

    Study Roadmap


    Hydros study forecasts of radar rfi

    Hydros Study Forecasts of Radar RFI

    < 1% of data has RFI > 1K

    (note: SMAPVEX08 shows more)


    Including rfi in smap error budget

    Including RFI in SMAP Error Budget

    • RFI impact on can be separated into two effects

      • Data Loss caused by detectable RFI (i.e. non-pulsed >~ 10 K*)

        • Examine CDF curves to forecast this level ~ 1%

        • Assume baseline detector catches majority of pulsed RFI

      • Error caused by non-detectable RFI (i.e. non pulsed < 10 K*)

        • Increases data product errors

        • Need to incorporate into radiometer RFI error budget

        • Problem: RFI not normally or uniformly distributed

    • Working to develop an error analysis to include this in a consistent way

      • Appears to be only weakly correlated to population density

      • Unsure of statistics outside CONUS

    • Improvement through different hardware

      • Could improve detectability of CW RFI with sub-banding and kurtosis.

      • Could mitigate CW RFI by downlinking sub-band and kurtosis data.

      • ~1 K threshold


    Airborne rfi information

    Airborne RFI Information

    • Numerous airborne and ground-based L-band campaigns have reported RFI

      • PALS/ESTAR/CoSMOS/ other ground based systems

      • Usually (except CoSMOS) unable to mitigate or detect low-level RFI

      • Mostly anecdotal evidence, detailed statistics not compiled

    • Several groups developing improved RFI detection/mitigation methods in recent years

    • Three RFI detecting/mitigating systems combined with JPL PALS in SMAPVEX08 campaign to provide enhanced dataset

      • Sept 20-Oct 19th, 2008, ~ 92 flight hours

      • ~20 deg. beamwidth, 40 deg. inc angle, Twin Otter aircraft

      • NASA P-3 also deployed with an RFI detecting radiometer from MSFC


    Smapvex08 deployment

    SMAPVEX08 Deployment

    • PALS radiometer measures L-band (1400-1420 MHz) brightnesses in H and V polarizations

    • Uses a dual-polarized L-band patch array antenna; two-sided 3 dB beamwidth ~ 20 deg, 40 deg. Inc. angle

      • rear facing orientation on the underside of Twin otter aircraft

      • nominal altitude 3000 m, nominal spot size 1.84 km x 0.87 km

    • Backend systems observe IF signals provided by PALS downconverter (200 MHz and 27 MHz cent freq’s)


    Pals flight summary for smapvex08

    PALS Flight Summary for SMAPVEX08

    • SMAPVEX08 from September 22 through October 19, 2008

      • Not inc. 1 week installation at Grand Junction

      • Total of 92 Flight hours

    • 21 PALS-ADD flights on the Twin Otter

      • 3 science flights in IOWA (12 flight hours)

      • 8 science flights in Delaware (37 flight hours)

      • 10 RFI/Transit flights (~ 20 flight hours)


    Pals adds rfi flights

    PALS/ADDS RFI Flights


    Rfi detection and mitigation in smapvex08

    RFI Detection and Mitigation in SMAPVEX08

    • Three algorithm types: pulse, cross-frequency, kurtosis

      • Pulse for pulsed sources, cross-freq for narrowband, kurtosis tests for normality

      • ~ 20-30 seconds for PALS to traverse one footprint

    • PALS: capable of pulse detection at ~ a few msec time scale

    • GSFC ADD: pulse detection at 2 usec time resolution

      • Also has a “pseudo-kurtosis” capability but not yet processed

      • No frequency resolution

    • U. Mich ADD: kurtosis or pulse detection >= 4 msec res

      • Has 8 x 2.29 MHz sub-bands, only fullband results presented here

    • OSU LISR: Records 350 usec x 0.1 MHz spectrograms

      • Pulse detection at 350 usec time resolution

      • Cross-frequency detection at 0.1 MHz spectral resolution

    • Selected RFI examples (among a huge number) follow (initial results)

      • Other spatial/polarization detection tests remain to be studied


    Pals rfi mitigation line 7 e w 3 oct 2008

    PALS RFI Mitigation (Line 7 E-W, 3 Oct 2008)

    • Most RFI encountered of the pulsed type

    • A median filtering pulse detection algorithm applied to PALS data at a few msec time resolution found effective

    • Approach being applied to PALS dataset to be distributed for soil moisture analysis

    • Approach ineffective for low-level or continuous RFI

    After Filtering


    Extreme cw rfi example new york

    Extreme CW RFI Example: New York

    Note: In band RFI!


    Apparent arsr 4 rfi gibbsboro new jersey

    Apparent ARSR-4 RFI: Gibbsboro, New Jersey

    Note: In band RFI!


    Adjacent band wmts rural virginia

    Adjacent band WMTS: Rural Virginia

    • 4876 community hospitals in CONUS (2004 US Census)

    • Average ~1 hospital per SMAP footprint, but they will clump

    Piepmeier - SDT Meeting #1


    Mapping airborne observations to smap

    Mapping Airborne Observations to SMAP

    • Scaling airborne results to SMAP requires consideration of

      • Larger range to SMAP

      • Larger antenna gain of SMAP

      • Larger footprint of SMAP

    • A Friis formula analysis shows that problem reduces to the EIRP per footprint area for either system (density of interferers equation)

      • See document mappingtosmap.pdf

    • SMAP forecasting reduces to averaging airborne detected RFI levels over scales comparable to SMAP footprint

    • Airborne tracks mostly linear, so compiling a SMAP footprint area would involve disjoint linear regions

      • Averaging over linear scales comparable to SMAP footprint diameter preferred?

      • Statistics compiled for multiple time scales to examine averaging effects


    Campaign statistics

    Campaign Statistics

    • Detected RFI levels using campaign dataset compiled for each RFI backend

      • GSFC: pulsed detection, H pol

      • UM ADD: kurtosis/pulsed detection, H and V pols

      • OSU LISR: pulsed, cross-freq detection, H and V pols

    • Time scales: 2 usec/4 msec/350 usec, then averaged to larger spatial scales

      • 30 seconds ~ 1 PALS footprint; 11 minutes ~ 1 SMAP footprint diameter

      • Entire flight ~ 1 SMAP footprint area

    • Some pixels (esp. in soil moisture study regions) observed multiple times

      • Improving processing to remove this effect

    • Also possible to examine “residual” RFI levels following various detection/mitigation approaches: subject for future discussions

    • Looking for basic consistency among multiple systems, then RFI info

      • Some RFI may still be undetected, some level of false alarms

      • Expected false alarm rate not coordinated here


    Pulsed rfi statistics from gsfc detector

    Pulsed-RFI Statistics from GSFC Detector

    ~15% of data has Pulsed RFI > 1K

    Mean RFI for each of 22 flights

    Mean RFI for each

    of 378 11-minute legs

    RFI in each of

    4.77M 20-ms samples

    Mean RFI for each of 8082 30-sec footprints


    Aggregate rfi statistics from umich add

    Aggregate-RFI Statistics from UMICH ADD

    ~15% of data has RFI > 1K

    (All flights)

    Piepmeier - SDT Meeting #1


    Rfi statistics osu lisr

    RFI statistics: OSU LISR

    • Pulsed> CW, V>H for Pulsed, H>V for CW

    • ~20% of pulsed > 1K

    • 10% of CW > 1 K at 11 minute scale, increases w/ integration


    Two forecasts

    Two Forecasts

    • Potential RFI from 2nd/3rd harmonics of TV stations

      • 2nd harmonics: Ch 52 and above -> these are going away Feb 2009

      • 3rd harmonics Ch 14

      • Strong 2nd harmonic of Ch 52 observed in SMAPVEX08 campaign

        • ~ 98 dB harmonic suppression observed, legal by FCC stds

      • No evidence of Ch 14 but no close overpasses

      • Result: 49 kW ERP = 0.1 K to SMAP, ~ 1.3% of US > 0.5 K RFI

    • Potential RFI from new 700 MHz Cellphone allocation

      • Replaces Ch 52 and above starting Feb 2009

      • Requires assumptions about cellphone harmonic suppression, market penetration, etc.

      • Estimate: ~ 4000 handsets/footprint = 1 K SMAP RFI

      • 5% of US has RFI > 0.5 K?


    Smapvex08 ch 52 harmonics

    SMAPVEX08 Ch 52 Harmonics

    • PALS/ADD Passed within 1500 m of KOLR Ch 52 Tx, Springfield MO

    • LISR Observed Spectrogram: harmonics 1396-1408 MHz (2x 698-704)

    • Friis formula analysis including PALS antenna properties and Tx information show ~ 98 dB suppression of harmonic (better than required)


    Potential channel 14 tv rfi

    Potential Channel 14 TV RFI

    ■ = 0.05-0.5 K (2.1 %)

    ■ = 0.5-5.0 K (1.3 %)

    ■ = >5.0 K (0.1%)

    SMAP RFI Review Telecon


    Potential 700 mhz wireless rfi

    Potential 700-MHz Wireless RFI

    ■ = 0.1-1.0 K (21 %)

    ■ = 1.0-10 K (2.2 %)

    ■ = >10 K (0.02%)

    SMAP RFI Review Telecon


    Cw rfi levels vs population density osu lisr

    CW RFI levels vs. Population Density (OSU LISR)

    • Detected CW RFIlevels (11 minute)correlated to a 2000 PopulationDensity database

    • Correlations~ 0.2-0.3

    • SignificantCW RFI observed innon-urban regions


    Summary and discussion

    Summary and Discussion

    • SMAPVEX08 campaign results show:

      • A large percent of observations contain negligible RFI

      • Pulsed RFI occurs frequently, baseline algorithm can handle much of this

      • CW RFI occurs less frequently, but up to 10% of SMAP footprint diameters estimated to have RFI >= 1 K

      • Additional analysis of these datasets still in progress

    • Measured data show that harmonic emissions are real, potential RFI from new 700 MHz cell phones a concern

    • Technical/program impact assessment of alternate hardware strategies against these sources in progress


    Including rfi in smap error budget1

    Including RFI in SMAP Error Budget

    • RFI impact on can be separated into two effects

      • Data Loss caused by detectable RFI (i.e. non-pulsed >~ 10 K*)

        • Examine CDF curves to forecast this level ~ 1%

        • Assume baseline detector catches majority of pulsed RFI

      • Error caused by non-detectable RFI (i.e. non pulsed < 10 K*)

        • Increases data product errors

        • Need to incorporate into radiometer RFI error budget

        • Problem: RFI not normally or uniformly distributed

    • Working to develop an error analysis to include this in a consistent way

      • Appears to be only weakly correlated to population density

      • Unsure of statistics outside CONUS

    • Improvement through different hardware

      • Could improve detectability of CW RFI with sub-banding and kurtosis.

      • Could mitigate CW RFI by downlinking sub-band and kurtosis data.

      • ~1 K threshold


    Summary and discussion 2

    Summary and Discussion (2)

    • Key issue for RFI is the eventual science impact!

    • To count data loss, RFI must be detectable

      • Can detect RFI>10 K without advanced hardware? C-band experience?

      • With 1-K detection threshold, >5% data might be lost.

    • Data loss requirements, repeated denial-of-service in fixed locations?

      • e.g. is it OK to always lose data over TV transmitters, hospitals, etc?

    • If we must go with baseline design

      • >5% data might have undetected RFI>1K

      • Would not meet error requirement on point-by-point basis

      • Would need to consider regional/global averages to meet requirement

    • Using a digital backend with sub-banding and kurtosis

      • CW RFI > 1K would be detectable

      • Some amount (90%?) would be removable

      • Could likely meet error requirement on point-by-point basis


    Recap

    Recap

    • Goal of this telecon: Peer review of analysis process and datasets

      • is analysis reasonable for forecasting SMAP RFI environment?

      • anything missing?

      • is the methodology reasonable?

  • Desired outcome:

    • Feedback from participants during telecon or via email within 24 hours

    • Progress toward consensus on any recommendations for SMAP

  • Now is the time to act! If SDT feels we won’t meet science needs with baseline design (i.e., must live with CW RFI), please speak up now.

  • SMAP RFI Review Telecon


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