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. In this paper, we illustrate the results of the work carried out within the context of RF-WIPE. Two test case scenarios have been investigated: a distributed sensor network-based instrument and networked planetary surface exploration. Each scenario is related to a particular network configuration. For such configurations, energy models and communication protocols have been developed, simulated, and validated both on laboratory tests and with outdoor field tests. Additionally, node deployment was investigated, and a deployment system based on a mobile robotics platform has been designed and tested.@en

We present a simulation framework for a 3-D high-resolution imaging radar at 300 GHz with mechanical scanning. This tool allows us to reproduce the imaging capabilities of the radar in different setups and with different targets. The simulations are based on a ray-tracing approximation combined with a bidirectional reflectance distribution function (BRDF) model for the scattering of rough surfaces. Moreover, we present a novel approach to estimate the scattering parameters of the BRDF model for different types of targets from the combination of the radar data and information obtained from an infrared structure light sensor. This new framework will serve as a baseline for the design of future radar multistatic configurations and to generate synthetic data to train automatic target recognition algorithms.@en