The Doppler Wind Experiment in the Optical Communications Era Kamal Oudrhiri, Sami Asmar and Bruce Moision June 20, 2013 International Planetary Probe Workshop – San Jose © 2013 California Institute of Technology. Government sponsorship acknowledged.

1.Radio Science (a brief background) 2.Doppler Wind Experiment (as an example) 3.Channel model for intensity-modulated optical signaling and photon- counting detectors 4.A comparison between radio science and optical science Parameter estimation via optical signals 2

The Start of Radio Science 3 It became apparent with early missions that occultations by planetary atmospheres would affect the quality of radio communications One person’s noise is another’s data One can study the atmospheric properties –And other aspects of planetary science, solar science, and fundamental physics A recognized field of solar system exploration with instrument distributed between the spacecraft and the ground stations

Radio Science Investigations 4 Utilize the telecommunication links between spacecraft and Earth to examine changes in the phase/frequency, amplitude, and polarization of radio signals to investigate: –Planetary atmospheres –Planetary rings –Planetary surfaces –Planetary interiors –Solar corona and wind –Comet mass flux –Fundamental Physics The measurements are conventionally made at the earth station.

Fundamental Limits on Sensitivity 5 1.Frequency stability 2.Amplitude stability 3.Signal to noise ratio 4.Intervening media 5.Spacecraft pointing stability and non gravitational forces 6.Navigation accuracy in predicting & reconstructing trajectory

Radio Science Experiment Types 6 Propagation –Study media –Remove the effects of forces Gravitation –Study forces –Remove the effects of media Observed changes can be very small

Radio Occultations 7 Study properties of planetary media along propagation path –Atmosphere: temperature-pressure profile –Ionosphere: electron density –Rings: particle structure and size distribution –Byproducts: planetary shapes Observables: –Amplitude and phase Refraction Scattering Edge diffraction Multi-path

Gravity & Planetary Interiors 8 Determine the mass and mass distribution –GM of body or system (planet + satellites) –Gravity field: higher order expansion of mass distribution Constrain models of internal structure –Examples: ocean on Europa Improve orbits and ephemeredes Observables: –Doppler and range: precise measurement of relative motion Doppler accuracy to ~ 0.03 mm/s at X-band and few microns/s at Ka-band Ranging accuracy to ~ 1 meter

Wind Profiles 9 Deduce wind speed and direction from Doppler when probe descends into atmosphere of planet or satellite –Huygens Probe at Titan –Galileo Probe into Jupiter –Russian probes at Venus Configuration: –Stable oscillators on probe and orbiter –Spacecraft-to-spacecraft links –May be able to receive signal on Earth determination of Titan‘s zonal wind speed along Huygens descent path

Huygens Doppler Experiment 10 Cassini Probe Support Avionics TCXO Receiving Channel B Transmitting data Carrier not stable Channel A Transmitting data Carrier stable Earth Radio Telescopes Receiving stable carrier “ Channel A on Earth ” Probe Support Avionics RUSO Not Receiving 2098 MHz RCP 2040 MHz LCP Direction: ~ 30 degrees Light Time: ~ 1 hr 7 min SNR: ~ 7 dBc Outside DSN band

Zonal Wind Retrieval 11 Doppler shift: where

DWE Results 12 Zonal winds West to East Turbulent above 100 km Strong wind: Maximum ~ 430 km/hr Shear layer km (10-50 mbar) –Strong positive/negative wind shear unexpected but evident in some GCMs Significant structure in lowest 5 km Huygens drifted 3.58º (158.3 km) eastward Wind results have implications for super rotational cyclostrophic flow

Tracking phase, frequency, and power of received signal enables: –spacecraft operation (range, velocity, power) –remote sensing –planetary science Future deep-space communications link may be at optical frequencies How accurately can we track the phase, frequency, and power of the optical signal? Science from a spacecraft 13 range: Velocity (from doppler): power fluctuations:

Science from an optical pulse train 14 Laser Transmitter Incident Power time Photon-counting photo-detector Photo- Electric Current time Use intensity modulation to transmit a train of pulses Detect light with a photon-counting detector, producing an impulse train corresponding to photo-electron arrival times Rate of photo-electrons is given by incident light intensity Estimate parameters of pulse train from photon-electron arrivals: phase, intensity, and frequency How well can we estimate phase, power, and frequency of an optical signal and how does this compare to estimation of analogous parameters from an RF signal?

Deep space optical communications links utilize intensity modulation and photon-counting –Power efficient with weak signals, not as sensitive as phase modulations to transmission through atmosphere We assume pulse train is provided by the communications link –Low duty cycles –High peak to average power ratio We assume pulse train pattern is known –Either from dedicated transmission time or reliable decoding of telemetry The Optical Communication signal 15 Typical duty cycles: 1/16 to 1/256Incident photon flux (power) Measured photon arrival times Signal photons Noise photons

Radio-Frequency (microwave) –Phase modulation (BPSK) –Coherent detection –Thermal noise limited –Small energy/photon –Large photons/pulse –Gaussian statistics Optical-Frequency (infrared) –Intensity modulation (pulse-position-modulation) –Non-coherent detection –Shot-noise limited –Large energy/photon –Small photons/pulse –Poisson statistics Radio- versus optical-frequency communication 16 Deep Space RF and Optical Communications links use different modulation and detection schemes and have different statistical models.

RF and Optical Parameter Estimation 17 Fundamental behavior is the same: differences depend on relative Power, Bandwidth, Noise PowerNoiseBandwidth

Assume representative values for an RF and optical (intensity-modulated+ direct- detection) Earth-Mars downlink link budget RF versus optical: comparison of link budgets 18 link equations:Ranging subcarrier mod. index Example: Optical/Ka-band: 32 dB gain in received power term, 16.5 dB loss in noise term, 54 dB gain in bandwidth term relative to range clock, 11 dB loss in bandwidth relative to carrier RMS errors go as the square-root: expect ~35 dB gain in range estimate, 8 dB gain in power estimate, 35 dB gain relative to range clock frequency,

Downlink (one-way) Estimation Error, Ka-Band RF versus optical: performance comparison 19 range frequency power Several orders magnitude gain in range estimation error Fractional frequency error worse relative to carrier, better relative to range clock 8 dB gain in power estimate ~37 dB ~8dB Power estimate doesn’t benefit from bandwidth gain Estimate from 1 MHz range clock Estimate from 32 GHz carrier ~8dB

Downlink (one-way) Estimation Error, S-Band, X-Band, Ka-Band, Infrared (optical) Doppler as a Function of Carrier Wavelength 20 We see gains over S, X-bands, loss relative to Ka-band Performance illustrated is power-limited error over one-way downlink. Complete comparison requires characterization of end-to-end optical Doppler link. However, results illustrate feasibility of utilizing optical communications link to extract Doppler measurements. 8.4 GHz (X-band) Estimate from 2.3 GHz carrier (S-band) 32 GHz (Ka-band) frequency To isolate dependence on carrier frequency, we held all other parameters constant (transmit, receive diameters, noise power) Two losses: smaller received power, and loss due to bandwidth reduction 1.55  m (infrared)

Presented framework for parameter estimation of intensity-modulated signal with photon-counting receivers –Represents current designs for deep-space optical communications link Compared Optical and RF one-way parameter estimation accuracy –Represents one component of range, Doppler, or power estimation –Illustrated large gains in range and power estimation –Gains in frequency (Doppler) estimation relative to S, X-bands, loss relative to Ka-band (for ideal systems in all cases) Conclusions/Discussion 21

Backup 22

Parameter estimation in radio frequencies utilize pure-tone signaling and coherent detection –Estimates based on observing a sinusoidal signal embedded in additive white Gaussian noise We consider parameter estimation from intensity-modulated (coherent state) optical waveforms and direct detection –Estimates based on photon arrival times given by a Poisson point process Radio- versus optical-frequency parameter estimation 23 Parameter estimation utilizes same capability as communication link.