Wireless sensor networks for planetary exploration: Experimental assessment of communication and deployment

Planetary surface exploration is an appealing application of wireless sensor networks that has been investigated in recent years by the space community, including the European Space Agency. The idea is to deploy a number of self-organizing sensor nodes forming a wireless networked architecture to provide a distributed instrument for the study and exploration of a planetary body. To explore this concept, ESA has funded the research project RF Wireless for Planetary Exploration (RF-WIPE), carried out by GMV, SUPSI and UPM. The purpose of RF-WIPE was to simulate and prototype a wireless sensor network in order to assess the potential and limitations of the technology for the purposes of planetary exploration.     

Prediction of ionospheric total electron content using adaptive neural network with in-situ learning algorithm

The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results.     

Identification and characterization of science-rich landing sites for lunar lander missions using integrated remote sensing observations

Despite more than 52 years of lunar exploration, a wide range of first-order scientific questions remain about the Moon’s formation, temporal evolution, and current surface and interior properties. Addressing many of these questions requires obtaining new in situ analyses or return of lunar surface or shallow subsurface samples, and hence rely on the selection of optimal landing sites. Here, we present an approach to optimize science-rich lunar landing site selection studies based on the integration of remote sensing observations. Currently available remote sensing data, as well as features of interest published in the recent literature, were integrated in a Geographic Information System. This numerical database contains geographic information about all these findings, which can be consulted and used to simultaneously display multiple features and parameters of interest. To illustrate our approach, we identified the optimal landing sites to address the two top priorities (or goals) relative to Concept 3 of the National Research Council of the National Academies (2007), namely to ‘Determine the extent and composition of the primary feldspathic crust, (ur)KREEP layer, and other products of differentiation’ and to ‘Inventory the variety, age, distribution and origin of lunar rock types’. We review site requirements and propose possible landing sites for both these goals. We identified 29 sites that best fulfill both these goals and compare them with the landing sites of planned future lunar lander missions. Finally, we detail two of these science-rich sites (Aristarchus and Theophilus craters) which are particularly accessible through their location on the nearside.     

Estimation of optical maturity parameter for lunar soil characterization using Moon Mineralogy Mapper (M3)

Prolonged exposure of the microscopic outer layer of the lunar surface to the space environment leads to the maturation of the surface. Maturation can be quantified and it may be expressed in terms of optical maturity (OMAT). Optical maturity estimations are very much helpful in the identification and mapping of the major minerals present on the lunar regolith. Estimation of the maturation and mineral mapping using remote sensing techniques are achieved, by coupling spectral reflectance of the lunar surface with an optimized origin. The present work estimates the optical maturity and Ferrous oxide content of the Goldschmidt and Schrodinger craters, through the recalibration of the classical method of Lucey et al. (2000a) with an origin of (0.08, 1.18) and Moon Mineralogy Mapper (M3) data. The overall recalibration results assure that the craters are highly matured.     

Precision analysis of autonomous orbit determination using star sensor for Beidou MEO satellite

This paper focuses on the autonomous orbit determination accuracy of Beidou MEO satellite using the onboard observations of the star sensors and infrared horizon sensor. A polynomial fitting method is proposed to calibrate the periodic error in the observation of the infrared horizon sensor, which will greatly influence the accuracy of autonomous orbit determination. Test results show that the periodic error can be eliminated using the polynomial fitting method. The User Range Error (URE) of Beidou MEO satellite is less than 2?km using the observations of the star sensors and infrared horizon sensor for autonomous orbit determination. The error of the Right Ascension of Ascending Node (RAAN) is less than 60?$\mathrm{\mu }\text{rad}$ and the observations of star sensors can be used as a spatial basis for Beidou MEO navigation constellation.