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Navigation of deep-space probes is accomplished through a variety of different radio observables, namely Doppler, ranging and Delta-Differential One-Way Ranging (Delta-DOR). The particular mix of observations used for navigation mainly depends on the available on-board radio system, the mission phase and orbit determination requirements. The accuracy of current ESA and NASA tracking systems is at level of 0.1 mais at 60 s integration time for Doppler, 1 to 5 m for ranging and 6 to 15 nrad for Delta-DOR measurements in a wide range of operational conditions. The ASTRA study, funded under ESA's General Studies Programme (GSP), addresses the ways to improve the end-to-end accuracy of Doppler, ranging and Delta-DOR systems by roughly a factor of 10. The target accuracies were set to 0.01 mm/s at 60 s integration time for Doppler, 20 cm for ranging and 1 nrad for Delta-DOR. The companies and universities that took part in the study were the University of Rome Sapienza, ALMASpace, BAE Systems and Thales Alenia Space Italy. The analysis of an extensive data set of radio-metric observables and dedicated tests of the ground station allowed consolidating the error budget for each measurement technique. The radio-metric data set comprises X/X, X/Ka and Ka/Ka range and Doppler observables from the Cassini and Rosetta missions. It includes also measurements from the Advanced Media Calibration System (AMCS) developed by JPL for the radio science experiments of the Cassini mission. The error budget for the three radio-metric observables was consolidated by comparing the statistical properties of the data set with the expected error models. The analysis confirmed the contribution from some error sources, but revealed also some discrepancies and ultimately led to improved error models. The error budget reassessment provides adequate information for building guidelines and strategies to effectively improve the navigation accuracies of future deep space missions. We report both on updated error budget for radio-metric observables and the system configurations proposed for the upgrade of ESA's tracking and orbit determination systems.

Deep space exploration missions are aimed at acquiring information about the solar system and a significant communication capacity has to be planned to transfer data for such very large distances. Terrestrial atmospheric impairments on the space-to-Earth propagating signal are the major responsible for the signal degradation thus reducing the channel temporal availability. In this work weather forecast models, coupled with microphysically-oriented radio-propagation models, are described in order to evaluate atmospheric effects at Ka-band. Estimation data return techniques are summarized and numerical results in a simulated operational scenario are illustrated in terms of received data volume using the BepiColombo mission as a baseline example.

A theoretical analysis for the evaluation of the probability of error occurring in resampling a noise-like band-limited Gaussian signal with flat power spectrum available through its digitized samples, is presented. The analysis assumes the use of an ideal sine-based interpolation algorithm for the digitized signal reconstruction, which is proved to be optimum for the considered class of signals and quantization functions. The particular case of lowest order, i.e., 1 bit, quantization function, is fully treated in analytical terms and a theoretical prediction for the error probability is derived. Validation of the presented analysis is made through a comparison with numerical simulations.

We investigate the effective microwave surface resistance of superconductor/dielectric/normal metal structures through numerical simulations, in particular, the setup with a thin superconducting film on a dielectric substrate, backed by a metallic endplate, is analysed, The results demonstrate that the effects of the substrate on the measured surface resistance of superconducting thin films must be taken into account. We show that in this setup several spurious effects are predicted. At characteristic frequencies, dependant on the substrate material, the commonly used thin-film approximation fails even for films thinner than the London penetration depth. In general, when the film is thicker than, but of the same order as, lambda, the measured surface resistance can exceed both the thin-film and the bulk values. We investigate how the effective surface resistance of a superconducting film can be approximated by the bulk or the thin-film value, as a function of frequency, film and substrate thickness, and temperature. We exploit a thin-film model to take into account the effects of the backing materials. Measurements of surface resistance are performed on a BSCCO/SrTiO3/metal structure to stress the nonintrinsic behaviours and to test the proposed model.

We study the reconstruction of a Gaussian random signal, subject to extreme clipping. The reconstruction is achieved by adding a high frequency sinusoidal reference signal prior to the hard-limiter, and by low pass filtering the output. Such a scheme belongs to the area of signal reconstruction from Sine Wave Crossings (SWC). In the present paper we study in detail the effect of sampling in time domain on the reconstruction algorithm, and we carry out an analysis, valid for high sampling rates, leading to approximate analytical expressions of the cross-correlation coefficient between the signal and its reconstructed version. As a result of our analysis, the best achievable cross-correlation coefficient, together with the corresponding setting of the configuration parameters, i.e., the frequency and power of the reference signal, is obtained as a function of the sampling rate. Asymptotic closed form formulas are derived in the limit of very large sampling rates.

A theoretical analysis, aimed at characterizing the degradation induced by the resampling and requantization processes applied to band-limited Gaussian signals with flat power spectrum, available through their digitized samples, is presented. The analysis provides an efficient algorithm for computing the complete {joint} bivariate discrete probability distribution associated to the true quantized version of the Gaussian signal and to the quantity estimated after resampling and requantization of the input digitized sequence. The use of Fourier transform techniques allows deriving {approximate} analytical expressions for the quantities of interest, as well as implementing their efficient computation. Numerical experiments are found to be in good agreement with the theoretical results, and confirm the validity of the whole approach., Submitted to Digital Signal Processing

We measure the microwave surface resistance R s at 24 GHz in BSCCO films of different thickness on two different substrates: LaAlO 3 and SrTiO 3 . One of the films deposited on LaAlO 3 presents an anomalous peak in R s at a temperature close to the transition temperature. The film deposited on SrTiO 3 presents multiple peaks as a function of the temperature. We show that both effects can be ascribed to substrate effects, instead of physical phenomena in the superconducting films. To reflect the actual experimental setup, we develop a simplified model for the microwave surface resistance of thin superconducting film deposited onto a dielectric substrate backed by a metal. We show that at characteristic frequencies, dependent on the substrate material, the commonly used thin film approximation fails even for films thinner than the London penetration depth. With this model, supplemented with a two-fluid expression for the conductivity, the single peak in the film deposited onto LaAlO 3 and the multiple peaks of the SrTiO 3 -deposited film find a quantitative explanation. The results demonstrate that the effects of the substrate on the measured R s of superconducting thin films must be taken into account.

In Very Large Baseline Interferometry, signals from far radio sources are simultaneously recorded at different antennas, with the purpose of investigating their physical properties. The recorded signals are generally modelled as realizations of Gaussian processes, whose power is dominated by the system noise at the receiving antennas. The actual signal coming from the radio source can be detected only after cross-correlation of the various data-streams. The signals received at each antenna are digitized after low noise amplification and frequency down-conversion, in order to allow subsequent digital post-processing. The applied quantization is coarse, 1 or 2 bits being generally associated to the signal amplitude. In modern applications the sampling is typically performed at a high rate, and subchannels are then generated by filtering, followed by decimation and re-quantization of the signal streams. The re-digitized streams are then cross-correlated to extract the physical observables. While the classical effect of quantization has widely been studied in the past, the de-correlation induced by the filtering and re-quantization process is still characterised experimentally, mainly due to its inherent mathematical complexity. In the present work we analyze the above problem, and provide algorithms and analytical formulas aimed at predicting the induced decorrelation for a wide class of quantization schemes, with the unique assumption of weakly correlated signals, typically fulfilled in VLBI and radio astronomy applications.