ORCID Profile
0000-0001-8819-0651
Current Organisation
University of Tasmania
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Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/EPSC2022-774
Abstract: & & We present an overview of the University of Tasmania& #8217 s (UTAS) progress in monitoring and providing ground support for space projects. With five radio telescopes distributed across Australia, UTAS has a good capacity to study a wide range of scientific phenomena in our Solar System and to improve the outcome of space missions. High-cadence Mars Express spacecraft observations in the X-band (8.4 GHz) were monitored between 2014 and 2022 using the European Very Long Baseline Interferometry (VLBI) network and UTAS radio telescopes to study interplanetary plasma scintillation and characterise solar wind parameters. The quantification of the plasma& #8217 s effect on the radio signal helps in phase referencing for ultra-precise spacecraft tracking. The international collaboration with the China National Space Administration (CNSA) also allowed simultaneous coherent tracking of the interplanetary plasma scintillation for the incoming radio signals of the Mars Express and Tianwen-1 spacecraft.& & & & Space weather monitoring has been carried out to study events such as coronal mass ejections using radio signals transmitted by the Solar Orbiter and Solar Heliospheric Observatory (SOHO) spacecraft. The unique radio telescope infrastructure at UTAS will be essential in providing ground support to the Planetary Radio Interferometry and Doppler Experiment (PRIDE) led by the Joint Institute for VLBI ERIC (JIVE). The PRIDE experiment has been chosen by the European Space Agency (ESA) for the JUpiter ICy Moons Explorer mission (JUICE) that will explore three of Jupiter& #8217 s moons: Europa, Ganymede, and Callisto. This space mission is scheduled to launch in April 2023.& & & & In addition, University of Tasmania has been conducting observations with NASA and JPL for bi-static radar tracking experiments to detect and monitor Near-Earth Asteroids. Over 14 observations have been conducting with UTAS radio telescopes since the beginning of 2021.& & & & & & & & & & & &
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/EPSC2022-653
Abstract: & & & strong& JUICE& #8217 s and Europa Clipper& #8217 s synergistic contribution to the Galilean moons& #8217 ephemerides & /strong& & & & & An accurate determination of the ephemerides of natural satellites is critical to our understanding of planetary systems& #8217 evolution, and of tidal dissipation mechanisms in particular. Diverse interior or dissipation-related parameters can be retrieved when reconstructing the moons& #8217 dynamics from space missions& #8217 radiometric data [1,2,3]. For the Galilean moons of Jupiter, unique challenges complicate the estimation of the satellites& #8217 dynamics, as the Laplace resonances between Io, Europa and Ganymede result in a strongly coupled dynamical problem. Efficiently improving the current ephemerides solution would thus ideally require a balanced data distribution between these moons. In this context, the synergy between the upcoming JUICE and Europa Clipper missions is of primary importance. In particular, the ~ 50 flybys at Europa to be performed by the Clipper spacecraft will efficiently supplement JUICE& #8217 s trajectory (2 flybys at Europa, 7 at Ganymede, 21 at Callisto, followed by a long orbital phase around Ganymede), leading to complementary radiometric data sets. Most importantly, in-system concurrent observations will be possible according to current missions& #8217 schedules. Preliminary analyses show that a joint solution from JUICE and Europa Clipper range and Doppler measurements can improve the estimation solution for the Galilean satellites& #8217 ephemerides and related dynamical properties (& em& e.g.& /em& tidal dissipation parameters) [4].& & & & & & & & & & strong& Contribution of PRIDE multi-spacecraft observations& /strong& & & & & The Planetary Radio Interferometry and Doppler Experiment (PRIDE) has been selected as one of the eleven experiments of the JUICE (JUpiter ICy moons Explorer) mission [5]. It relies on Very Long Baseline Interferometry (VLBI) techniques to process radiometric signals used for tracking, communications and/or radioscience. The main PRIDE observables are measurements of the spacecraft& #8217 s lateral position with respect to a phase calibrator (VLBI), expressed in the ICRF (International Celestial Reference Frame) [6], but by-product Doppler observables are also generated [7]. Compared to range and Doppler measurements, which are collected in the line-of-sight direction, PRIDE VLBI data are sensitive to the spacecraft& #8217 s position in the two other directions. They thus provide very complementary information, which could help achieving an improved solution for the spacecraft& #8217 s and moons& #8217 states in particular. A previous analysis has quantified the contribution of PRIDE VLBI observations to the Galilean moons& #8217 ephemerides for the JUICE test case [2], using a simplified non-coupled model [8]. A noticeable improvement was indeed obtained in the out-of-plane direction when including VLBI data, especially for Ganymede and Callisto (more data collected at these two moons).& & & & In addition to these single-spacecraft VLBI measurements, the concurrent in-system tracking of the JUICE and Europa Clipper spacecraft will offer PRIDE a unique opportunity to fully exploit the synergy between the two missions& #8217 trajectories. If the two spacecraft are both visible from a telescope (& em& i.e.& /em& in-beam or within the same telescope beam) and simultaneously transmitting a radio signal, it is possible to realise multi-spacecraft VLBI observations, which will directly provide accurate measurements of the relative lateral position of the two spacecraft (right ascension and declination difference in the ICRF). Such observations were already successfully collected between several Martian orbiters (MRO, MEX, TGO) in 2019 [9], as shown in Figure 1.& & & & & em& & img src=& quot & quot alt=& quot & quot width=& quot & quot height=& quot & quot /& & /em& & & & & & em& Figure 1: Simultaneous detection of the signals transmitted by several Martian orbiters and landers, from [9].& /em& & & & & Given the synergistic nature of the JUICE and Clipper trajectories, the signals of the two spacecraft are expected to be visible in the same beam of ground-based telescopes during a significant fraction of their Jovian tours. Looking in detail at JUICE and Clipper flybys& #8217 sequences displayed in Figure 2, the two spacecraft will occasionally perform near-simultaneous flybys around different moons, with typically only a coupled of days between JUICE& #8217 s and Clipper& #8217 s flybys. The relative angular measurements derived from PRIDE VLBI observations might then translate into direct constraints of the moons& #8217 relative states, expected to be extremely valuable for the ephemerides solutions.& & & & & img src=& quot & quot alt=& quot & quot width=& quot & quot height=& quot & quot /& & & & & & em& Figure 2: JUICE and Europa Clipper trajectories (altitude with respect to the moons)& /em& & & & & & & & & & & strong& Contribution of & /strong& & strong& these& /strong& & strong& observations to the Galilean satellites& #8217 ephemerides & /strong& & & & & Our open-source estimation tool (Tudat(py)& sup& & /sup& ) is now able to concurrently simulate several missions with different trajectories and observation schedules in a single estimation [10]. It is also linked with a VLBI prediction tool allowing to search for suitable phase calibrators close to the spacecraft at any potential observation epoch. Using these functionalities, we perform a simulation study to quantify the potential contribution of multi-spacecraft (in-beam) VLBI observations, also analysing its sensitivity to the observations& #8217 cadence and accuracy.& & & & To this end, we will investigate when multi-spacecraft VLBI observations could be obtained from the JUICE and Clipper spacecraft. This involves searching for suitable phase calibrators and ensuring that both spacecraft are visible from ground telescopes at a given epoch. We will also further investigate promising observation geometries (& em& e.g.& /em& near-simultaneous flybys, see Figure 2). The simulated multi-spacecraft observables will then be added to a joint JUICE & #8211 Clipper estimation, which also includes & em& nominal & /em& & em& (& /em& single-spacecraft) PRIDE VLBI observations for JUICE.& & & & We will then precisely quantify the contribution of these simulated multi-spacecraft VLBI observations to the estimation solution, focusing on the moons& #8217 ephemerides and related dynamical parameters in particular. If proven beneficial, this will motivate the acquisition of such observations during the JUICE and Clipper missions. Additionally, our sensitivity analysis will provide direct recommendations regarding observation scheduling. If need be (& em& i.e.& /em& particularly interesting multi-spacecraft VLBI observation but no phase calibrator in the angular vicinity of the spacecraft), this could also highlight the need to search for yet unknown calibrators in a certain region of the sky.& & & & & & & & & & strong& References& /strong& & & & & [1] Dirkx et al., Planetary and Space Science 134 (2016): 82-95.& & & & [2] Dirkx et al., Planetary and Space Science 147 (2017): 14-27.& & & & [3] Lainey et al., Nature Astronomy 4.11 (2020): 1053-1058.& & & & [4] A. Magnanini et al., in preparation.& & & & [5] Gurvits et al., European Planetary Science Congress (2013).& & & & [6] Duev et al., Astronomy & Astrophysics 541 (2012): A43.& & & & [7] Bocanegra-Bahamon et al., Astronomy & Astrophysics 609 (2018): A59.& & & & [8] Fayolle et al., submitted to Planetary & Space science (under revision)& & & & [9] Molera Calv& #233 s et al., Publications of the Astronomical Society of Australia, 38, E065& & & & [10] Fayolle et al., EGU General Assembly (2022).& & & & & & & & id=& quot sdfootnote1& quot & & / & & id=& quot sdfootnote2& quot & & class=& quot sdfootnote& quot & & em& & sup& & /sup& docs.tudat.space& /em& & & & / & & id=& quot sdfootnote1& quot & & / & & id=& quot sdfootnote2& quot & & class=& quot sdfootnote& quot & & & & & / &
Publisher: EDP Sciences
Date: 09-2016
Publisher: Elsevier BV
Date: 08-2018
Publisher: Frontiers Media SA
Date: 23-05-2023
DOI: 10.3389/FRSPT.2023.1162915
Abstract: Space debris are composed of both natural and human made objects, some in near Earth orbits while others are passing through deep space. Asteroids may represent one form of near Earth and deep space debris. In this article we report on a set of asteroid observations from the southern hemisphere. We indicate that Apollo and Aten class asteroids represent another form of deep space debris of a potentially hazardous nature to orbiting spacecraft and/or Earth based locations. We also show some of the operational challenges, types of facilities and the importance of geographic ersity, that is, necessary for detecting, observing and characterising asteroids, especially PHA’s. For many years, space agencies and institutions have observed and monitored near Earth asteroids and objects (NEO’s) using high gain radio frequency antennas and optical telescopes in the northern hemisphere (GSSR, Arecibo, Catalina, Pan-STARRS, Atlas and Linear) 1) However a regular operational system to monitor the southern skies does not have the same level of maturity and is where a percentage of asteroids and various human made objects are not detected until they pass into northern skies. To fill that gap the Southern Hemisphere Asteroid Radar Program (SHARP) 2) located in Australia uses available antenna time on either a 70 or 34 m beam waveguide antenna located at the Canberra Deep Space Communication Complex (CDSCC) to transmit a Doppler compensated continuous radio wave at 2.114 GHz (14.2 cm) and 7.15945 GHz (4.2 cm) toward the NEO and receive its echoes at the 64 m Parkes or 6 m × 22 m Australia Telescope Compact Array (ATCA) antennas at Narrabri in Australia. This mode of NEO observation is termed a deep space bistatic radar. The southern hemisphere program has also recently been joined by the 12 m University of Tasmania antennas at Hobart (Tasmania) and Katherine (Northern Territory). Combining SHARPS bistatic radar with small optical apertures located at the University of New South Wales (UNSW) and University of Western Australia (UWA) allows combined optical/RF NEO detections. Whilst sub-metre class optical instruments have contributed independently to asteroid detection over decades, the use of coordinated small 0.3–0.5 m instruments synchronized to large asteroid radars offers an observational flexibility and adaptability when larger optical systems 3) are dedicated to other forms of professional optical astronomy. Since 2015, SHARP has illuminated and tracked over 30 NEO’s ranging in diameter from 7 to 5000 m at ranges of 0.1–18 lunar distances (LD) from Australia.
Publisher: Sissa Medialab
Date: 21-02-2019
DOI: 10.22323/1.344.0060
Publisher: IEEE
Date: 07-2018
Publisher: Springer Science and Business Media LLC
Date: 04-07-2013
DOI: 10.1007/S00345-013-1125-0
Abstract: To evaluate the temporal relationship between interval to biochemical recurrence (BCR) following radical prostatectomy (RP) and prostate cancer-specific mortality (PCSM). The study comprised of 2,116 men from the Victorian Radical Prostatectomy Register, a whole-of-population database of all RPs performed between 1995 and 2000 in Victoria, Australia. Follow-up prostate-specific antigen and death data were obtained via record linkage to pathology laboratories and the Victorian Registry of Births, Deaths and Marriages. Poisson regression models with PCSM as the outcome were fit to the data. Models included age at surgery, Gleason score and tumour stage as covariates. Median post-surgery and post-BCR follow-up was 10.3 and 7.5 years, respectively. 695 men (33 %) experienced BCR during follow-up, of which 82 % occurred within 5 years of RP 66 men died from prostate cancer. Men with combined high Gleason sum (≥4 + 3) and extra-prostatic (≥pT3a) disease had substantially increased mortality rate with early BCR, while those experiencing BCR after a longer interval had significantly lower mortality. Men with combined low Gleason sum (≤3 + 4) and organ-confined disease (≤pT2c) risk disease were not at any substantial risk of death in this time frame regardless of timing of BCR following RP. This study evaluates the temporal relationship between BCR and PCSM using a whole-of-population cohort of men treated with RP. Men with low-risk features of prostate cancer at time of RP have low mortality even if they experience early BCR. This subgroup may be counselled regarding their favourable long-term prognosis.
Publisher: Sissa Medialab
Date: 22-01-2016
DOI: 10.22323/1.178.0030
Publisher: Sissa Medialab
Date: 21-05-2015
DOI: 10.22323/1.230.0050
Publisher: Sissa Medialab
Date: 18-02-2019
DOI: 10.22323/1.344.0059
Publisher: Sissa Medialab
Date: 21-02-2019
DOI: 10.22323/1.344.0139
Publisher: Cambridge University Press (CUP)
Date: 2021
DOI: 10.1017/PASA.2021.56
Abstract: We present a software package for single-dish data processing of spacecraft signals observed with VLBI-equipped radio telescopes. The Spacecraft Doppler tracking (SDtracker) software allows one to obtain topocentric frequency detections with a sub-Hz precision and reconstructed and residual phases of the carrier signal of any spacecraft or landing vehicle at any location in the Solar System. These data products are estimated using the ground-based telescope’s highly stable oscillator as a reference, without requiring an a priori model of the spacecraft dynamics nor the downlink transmission carrier frequency. The software has been extensively validated in multiple observing c aigns of various deep space missions and is compatible with the raw s le data acquired by any standard VLBI radio telescope worldwide. In this paper, we report the numerical methodology of SDtracker, the technical operations for deployment and usage, and a summary of use cases and scientific results produced since its initial release.
Publisher: WORLD SCIENTIFIC
Date: 16-11-2017
Publisher: Sissa Medialab
Date: 10-11-2009
DOI: 10.22323/1.082.0083
Publisher: Cambridge University Press (CUP)
Date: 2023
DOI: 10.1017/PASA.2023.12
Abstract: The radio signal transmitted by the Mars Express (MEX) spacecraft was observed regularly between the years 2013–2020 at X-band (8.42 GHz) using the European Very Long Baseline Interferometry (EVN) network and University of Tasmania’s telescopes. We present a method to describe the solar wind parameters by quantifying the effects of plasma on our radio signal. In doing so, we identify all the uncompensated effects on the radio signal and see which coronal processes drive them. From a technical standpoint, quantifying the effect of the plasma on the radio signal helps phase referencing for precision spacecraft tracking. The phase fluctuation of the signal was determined for Mars’ orbit for solar elongation angles from 0 to 180 deg. The calculated phase residuals allow determination of the phase power spectrum. The total electron content of the solar plasma along the line of sight is calculated by removing effects from mechanical and ionospheric noises. The spectral index was determined as $-2.43 \\pm 0.11$ which is in agreement with Kolmogorov’s turbulence. The theoretical models are consistent with observations at lower solar elongations however at higher solar elongation ( $ $ 160 deg) we see the observed values to be higher. This can be caused when the uplink and downlink signals are positively correlated as a result of passing through identical plasma sheets.
Publisher: Zenodo
Date: 2021
Publisher: Sissa Medialab
Date: 21-05-2015
DOI: 10.22323/1.230.0113
Publisher: Copernicus GmbH
Date: 08-10-2020
DOI: 10.5194/EPSC2020-647
Abstract: & & & strong& Introduction& /strong& & & & & Planetary Radio Interferometry and Doppler Experiment (PRIDE) will exploit the signal recording and processing technology developed originally for Very Long Baseline interferometric (VLBI). The essence of PRIDE is in observing the spacecraft radio signal with a network of Earth-based radio telescopes. The PRIDE technique developed at the Joint Institute for VLBI ERIC (JIVE) together with its partners was used for several experiments with several ESA planetary science missions. It has been chosen by ESA as one of the eleven experiments of the Jupiter Icy Moons Explorer (JUICE), the first Large-class mission in the ESA& #8217 s Cosmic Vision 2015& #8211 program. The mission is scheduled for launch in 2022.& & & & & img src=& quot data:image/jpeg base64, 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 r+GHM3h7QZ35km0fTZW3ckGSztmYAnnBJJ9zzjOK/zH/B0c3jz9oHw4qBp5/F/xh0o71y7yPr/ja1kcgICWeVrgkAZ3NwM1 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quot alt=& quot & quot /& & & & & & em& & strong& Figure 1: A typical PRIDE Experiment & /strong& & /em& & & & & & & strong& . PRIDE for JUICE Mission& /strong& & & & & PRIDE is a multidisciplinary component of the JUICE science suite. The main measured deliverables of PRIDE are lateral coordinates L(& #966 , & #952 ) of spacecraft and the radial velocity of spacecraft. The former (main) measurable of PRIDE requires the phase-referencing VLBI observations (see Duev et al. [1]).& The latter employs the Doppler extraction technique described in [7,8]. Its prime deliverable will be used primarily for improvement of the Jovian system ephemerides in support to a variety of applications, ranging from gravimetry to geodynamics to fundamental physics. A typical PRIDE experiment is shown in Figure 1.& & & & PRIDE observations are carried out by the ground segment when the spacecraft emits a radio signal for communication or radio science purposes. The methodology of PRIDE has been demonstrated with the ESA's Venus Express & Mars Express by Duev et al. 2012 [1], Molera Calv& #233 s et al. 2014 [2], Duev et al. 2016 [4], Molera Calv& #233 s et al. 2017 [8], Bocanegra et al. 2018 [7], Bocanegra et al. 2019 [8]. In addition to nominal science objectivities, the team is considering ad hoc observations around the Venus and Mars flybys during the cruise phase. A covariance analysis for a broad scope of the PRIDE measurable and Jovian system parameters have been performed to optimize the observation planning and the overall science impact of the JUICE mission (Dirkx. D et al. 2016 [4] & Dirkx. D et al. 2017 [5]).& & & & & strong& . Ongoing PRIDE observing projects& /strong& & & & & In 2020, we have begun a c aign to gather PRIDE tracking data of the InSight lander, with up to three tracking epochs scheduled for each of the three 2020 EVN sessions. The primary goal of these experiments is to serve as a preparatory activity for more intensive operations for ExoMars-LaRa [9].& & & & The primary manner in which PRIDE will be able to contribute to the science objectives of LaRa is through the use of the Doppler data obtained by all receiving stations. As shown by the analysis of Bocanegra et al. 2018 [7], PRIDE Doppler data at X-band is similar, and in some cases, higher quality compared to regular closed-loop tracking data. One advantage of PRIDE lies in its ersity of receiving stations, making the data quality much less dependent on the meteorological or operational conditions at a single telescope.& & & & & img src=& quot data:image/jpeg base64, 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& quot alt=& quot & quot /& & & & & & strong& & em& Figure 2: & Frequency detections of Mars Insight (NASA) as seen from the University of Tasmania network of radio telescopes. The spacecraft signal was detected at the sky frequency of 8407.25 MHz and Doppler shift of 50 kHz along 70 minutes of tracking& /em& & /strong& & & & & Besides, we propose to merge the three-way Doppler data generated at each receiving station, to fully exploit the data encoded in all downlinks. This would allow an improved calibration of receiving station noise levels, as the noise incurred locally at the receiving ground station (including Earth tropospheric and ionospheric noise on the downlink) is independent for each station. We will use the InSight tracking data to test different practical approached to achieving this conceptual aim, and apply these & #8216 lessons learned& #8217 to ExoMars-LaRa data analysis. In particular, we will apply the InSight tracking data to investigate different manners in which to weigh the data, considering the fact that the three-way Doppler noise generated at all receiving stations is highly correlated, and to investigate how inter-station biases and drifts can best be calibrated for. The results of the ongoing PRIDE observations of NASA's InSight probe will be reported elsewhere (Dirkx. D, Le Maistre. S, Dehant. V et al., in preparation).& & & & & strong& References& /strong& & & & ol& & li& D. A. Duev. et al.: Spacecraft VLBI and Doppler tracking: algorithms and implementation, Astronomy & Astrophysics, 541, A43, 2012& /li& & li& G. Molera Calves. et al.: Observations and analysis of phase scintillation of spacecraft signal on the interplanetary plasma& quot . In: A& A 564, A4, 2014& /li& & li& D. A. Duev. et al.: Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique: A test case of the Mars Express Phobos fly-by, Astronomy & Astrophysics, Vol. 593, 2016& /li& & li& Dirkx D. et al.: Dynamical modeling of the Galilean moons for the JUICE mission, Planetary and Space Science 134 (2016) 82& #8211 & /li& & li& Dirkx D. et al.: On the contribution of PRIDE JUICE to Jovian system ephemerides, Planetary and Space Science, 14& #8211 , 2017& /li& & li& G. Molera Calves. et al.: Analysis of an Interplanetary Coronal Mass Ejection by a Spacecraft Radio Signal: A Case Study, Space Weather, 15& /li& & li& T. M. Bocanegra-Baham& #243 n. et al.: Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique: A test case of the Mars Express Phobos Flyby II. Doppler tracking: Formulation of observed and computed values, and noise budget, Astronomy & Astrophysics, 609, A59, 2018& /li& & li& T.M. Bocanegra-Baham& #243 n. et al.: Venus Express radio occultation observed by PRIDE, Astronomy & Astrophysics, 624, A59, 2019& /li& & li& Dehant V. et al.: The radioscience LaRa instrument onboard ExoMars 2020 to investigate the rotation and interior of Mars, Planetary and Space Science 180, 104776, 2020& /li& & /ol& & & & & & & & & & &
Publisher: Elsevier BV
Date: 11-2021
Publisher: Oxford University Press (OUP)
Date: 28-06-2023
Abstract: Astronomers have studied the Vela pulsar (PSR J0835−4510) for decades. This study analyses almost 100 h of single-pulse data collected over three consecutive days in 2016 and 2020. The work investigates the fascinating phenomena of the earlier arrival of brighter pulses with their increase in peak intensity. We found a hyperbolic relation between them by constructing integrated pulse profiles using flux density intervals and examining the relationship between pulse arrival time and intensity. We identified a phase limit of −0.85 ± 0.0109 ms for the earliest arrival of the brightest pulses. This study offers exciting prospects for further exploring the emission regions responsible for the Vela pulsar’s regular pulses and giant micro-pulses.
Publisher: American Astronomical Society
Date: 25-11-2022
Abstract: Probing the solar corona is crucial to study the coronal heating and solar wind acceleration. However, the transient and inhomogeneous solar wind flows carry large- litude inherent Alfvén waves and turbulence, which make detection more difficult. We report the oscillation and propagation of the solar wind at 2.6 solar radii (Rs) by observation of China’s Tianwen and ESA’s Mars Express with radio telescopes. The observations were carried out on 2021 October 9, when one coronal mass ejection (CME) passed across the ray paths of the telescope beams. We obtain the frequency fluctuations (FFs) of the spacecraft signals from each in idual telescope. First, we visually identify the drift of the frequency spikes at a high spatial resolution of thousands of kilometers along the projected baselines. They are used as traces to estimate the solar wind velocity. Then we perform the cross-correlation analysis on the time series of FF from different telescopes. The velocity variations of solar wind structure along radial and tangential directions during the CME passage are obtained. The oscillation of tangential velocity confirms the detection of a streamer wave. Moreover, at the tail of the CME, we detect the propagation of an accelerating fast field-aligned density structure indicating the presence of magnetohydrodynamic waves. This study confirms that the ground-station pairs are able to form particular spatial projection baselines with high resolution and sensitivity to study the detailed propagation of the nascent dynamic solar wind structure.
Publisher: Copernicus GmbH
Date: 23-09-2022
DOI: 10.5194/EPSC2022-342
Abstract: & class=& quot co_mto_htmlabstract mt-3& quot & & class=& quot co_mto_htmlabstract-affilitions& quot & & & The Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique utilize signal recording and processing technology developed originally for Very Long Baseline Interferometry (VLBI) to determine spacecraft lateral position in ICRF, as an extension of conventional radio-tracking techniques [1]. The essence of the PRIDE technique is in observing the spacecraft radio signal with a network of Earth-based radio telescopes. The PRIDE technique, developed at the Joint Institute for VLBI ERIC (JIVE), has been used for numerous experiments with several ESA planetary science missions. PRIDE has been selected by ESA as one of the eleven science experiments of the Jupiter Icy Moons Explorer (JUICE), the L-class mission scheduled for launch in 2023.& & & / & & class=& quot co_mto_htmlabstract-content mt-3& quot & & & & img src=& quot & quot alt=& quot & quot width=& quot & quot height=& quot & quot /& & & & & & strong& Figure 1: PRIDE Experiment for the JUICE Mission& /strong& & & & & The main observables of PRIDE are ultra-precise estimates of spacecraft lateral position based on the phase referenced VLBI tracking and radial Doppler measurements [3,7,8]. The methodology of PRIDE has been proven and validated with the ESA's Venus Express & Mars Express [1,2,4,7,8,9]. PRIDE will contribute to the determination of the JUICE spacecraft state vector and the improvement of the Galilean satellite ephemerides [4,5,10]. It is worth noticing the synergistic nature of PRIDE measurements to other key experiments of the JUICE mission, in particular addressing the major science goals of the mission.& & & & & & & & & & img src=& quot & quot alt=& quot & quot width=& quot & quot height=& quot & quot /& & & & & & strong& Figure 2: PRIDE Experiment Planning chart during the cruise phase of the JUICE Mission. Blue dots indicate celestial positions of radio sources with well-defined coordinates suitable for phase-referencing VLBI [reference on the Petrov's online catalogue]. Red dots show those potential reference sources located within 1 degree to the celestial track of the JUICE spacecraft.& /strong& & & & & The paper will describe the analysis and implementation of system engineering methodologies of the PRIDE inputs into the operational design of the JUICE mission. A particular emphasis will be given on cross instrumental analysis and consolidation of top synergies of PRIDE with other JUICE experiments using the ESA science planning tools. The paper will demonstrate advanced methods of efficient experiment planning and block scheduling. The identification of celestial areas of interest in near-field VLBI (PRIDE) observations of interplanetary spacecraft on all phases of their missions, especially during the cruise phase will also be covered in this paper.& & & & & strong& References& /strong& & & & & ) D. A. Duev. et al.: Spacecraft VLBI and Doppler tracking: algorithms and implementation, Astronomy & Astrophysics, 541, A43, 2012& & & & ) G. Molera Calves. et al.: Observations and analysis of phase scintillation of spacecraft signal on the interplanetary plasma& quot . In: A& A 564, A4, 2014& & & & ) D. A. Duev. et al.: Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique: A test case of the Mars Express Phobos fly-by, Astronomy & Astrophysics, Vol. 593, 2016& & & & ) Dirkx D. et al.: Dynamical modeling of the Galilean moons for the JUICE mission, Planetary and Space Science 134 (2016) 82& #8211 & & & & ) Dirkx D. et al.: On the contribution of PRIDE JUICE to Jovian system ephemerides, Planetary and Space Science, 14& #8211 , 2017& & & & ) G. Molera Calves. et al.: Analysis of an Interplanetary Coronal Mass Ejection by a Spacecraft Radio Signal: A Case Study, Space Weather, 15& & & & ) T. M. Bocanegra-Baham& #243 n. et al.: Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique: A test case of the Mars Express Phobos Flyby II. Doppler tracking: Formulation of observed and computed values, and noise budget, Astronomy & Astrophysics, 609, A59, 2018& & & & ) T. M. Bocanegra-Baham& #243 n. et al.: Venus Express radio occultation observed by PRIDE, Astronomy & Astrophysics, 624, A59, 2019& & & & ) G. Molera Calv& #233 s. Et a.: High spectral resolution multi-tone Spacecraft Doppler tracking software: Algorithms and implementations, Publications of the Astronomical Society of Australia (PASA), 2021& & & & ) Fayolle et al., Decoupled and coupled moons& #8217 ephemerides estimation strategies - Application to the JUICE mission, submitted to Planetary & Space Science, 2022& & & & & & & & & & & & & !-- COMO-HTML-CONTENT-END --& & / & & / &
Publisher: EDP Sciences
Date: 26-04-2012
Publisher: Cambridge University Press (CUP)
Date: 08-2009
DOI: 10.1017/S1743921310001663
Abstract: The presence of water has been considered for a long time as a key condition for life in planetary environments. The Cassini mission discovered water vapour in the Kronian system by detecting absorption of UV emission from a background star (Hansen et al . 2006). Prompted by this discovery, we started an observational c aign for search of another manifestation of the water vapour in the Kronian system, its maser emission at the frequency of 22 GHz (1.35 cm wavelength). Observations with the 32 m Medicina radio telescope (INAF-IRA, Italy) started in 2006 using Mk5A data recording and the JIVE-Huygens software correlator. Later on, an on-line spectrometer was used at Medicina. The 14 m Metsähovi radio telescope (TKK-MRO, Finland) joined the observational c aign in 2008 using a locally developed data capture unit and software spectrometer. More than 300 hours of observations were collected in 2006-2008 c aign with the two radio telescopes. The data were analysed at JIVE using the Doppler tracking technique to compensate the observed spectra for the radial Doppler shift for various bodies in the Kronian system (Pogrebenko et al . 2009). Here we report the observational results for Hyperion, Titan, Enceladus and Atlas, and their physical interpretation. Encouraged by these results we started a c aign of follow up observations including other radio telescopes.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: IOP Publishing
Date: 27-07-2023
Abstract: The Einstein Equivalence Principle (EEP) is a cornerstone of general relativity and predicts the existence of gravitational redshift. We report on new results of measuring this shift with RadioAstron (RA), a space very long baseline interferometry (VLBI) spacecraft launched into an evolving high eccentricity orbit around Earth with geocentric distances reaching 353 000 km. The spacecraft and ground tracking stations at Pushchino, Russia, and Green Bank, USA, were each equipped with a hydrogen maser frequency standard allowing a possible violation of the predicted gravitational redshift, in the form of a violation parameter ɛ , to be measured. By alternating between RA’s frequency referencing modes during dedicated sessions between 2015 and 2017, the recorded downlink frequencies can essentially be corrected for the non-relativistic Doppler shift. We report on an analysis using the Doppler-tracking frequency measurements made during these sessions and find ε = ( 2.1 ± 3.3 ) × 10 − 4 . We also discuss prospects for measuring ɛ with a significantly smaller uncertainty using instead the time-domain recordings of the spacecraft signals and envision how 10 −7 might be possible for a future space VLBI mission.
Publisher: Springer Science and Business Media LLC
Date: 04-03-2015
Publisher: American Geophysical Union (AGU)
Date: 11-2017
DOI: 10.1002/2017SW001701
Publisher: EDP Sciences
Date: 2015
Publisher: EDP Sciences
Date: 2018
DOI: 10.1051/0004-6361/201731524
Abstract: Context. Closed-loop Doppler data obtained by deep space tracking networks, such as the NASA Deep Space Network (DSN) and the ESA tracking station network (Estrack), are routinely used for navigation and science applications. By shadow tracking the spacecraft signal, Earth-based radio telescopes involved in the Planetary Radio Interferometry and Doppler Experiment (PRIDE) can provide open-loop Doppler tracking data only when the dedicated deep space tracking facilities are operating in closed-loop mode. Aims. We explain the data processing pipeline in detail and discuss the capabilities of the technique and its potential applications in planetary science. Methods. We provide the formulation of the observed and computed values of the Doppler data in PRIDE tracking of spacecraft and demonstrate the quality of the results using an experiment with the ESA Mars Express spacecraft as a test case. Results. We find that the Doppler residuals and the corresponding noise budget of the open-loop Doppler detections obtained with the PRIDE stations compare to the closed-loop Doppler detections obtained with dedicated deep space tracking facilities.
Publisher: EDP Sciences
Date: 04-2019
DOI: 10.1051/0004-6361/201833160
Abstract: Context . Radio occultation is a technique used to study planetary atmospheres by means of the refraction and absorption of a spacecraft carrier signal through the atmosphere of the celestial body of interest, as detected from a ground station on Earth. This technique is usually employed by the deep space tracking and communication facilities (e.g., NASA’s Deep Space Network (DSN), ESA’s Estrack). Aims . We want to characterize the capabilities of the Planetary Radio Interferometry and Doppler Experiment (PRIDE) technique for radio occultation experiments, using radio telescopes equipped with Very Long Baseline Interferometry (VLBI) instrumentation. Methods . We conducted a test with ESA’s Venus Express (VEX), to evaluate the performance of the PRIDE technique for this particular application. We explain in detail the data processing pipeline of radio occultation experiments with PRIDE, based on the collection of so-called open-loop Doppler data with VLBI stations, and perform an error propagation analysis of the technique. Results . With the VEX test case and the corresponding error analysis, we have demonstrated that the PRIDE setup and processing pipeline is suited for radio occultation experiments of planetary bodies. The noise budget of the open-loop Doppler data collected with PRIDE indicated that the uncertainties in the derived density and temperature profiles remain within the range of uncertainties reported in previous Venus’ studies. Open-loop Doppler data can probe deeper layers of thick atmospheres, such as that of Venus, when compared to closed-loop Doppler data. Furthermore, PRIDE through the VLBI networks around the world, provides a wide coverage and range of large antenna dishes, that can be used for this type of experiments.
Publisher: Pleiades Publishing Ltd
Date: 11-2011
Publisher: EDP Sciences
Date: 14-01-2009
Publisher: No publisher found
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Start Date: 2021
End Date: 2021
Funder: FrontierSI
View Funded Activity