Application of the Evidence Procedure to the Estimation of Wireless Channels

We address the application of the Bayesian evidence procedure to the estimation of wireless channels. The proposed scheme is based on relevance vector machines (RVM) originally proposed by M. Tipping. RVMs allow to estimate channel parameters as well as to assess the number of multipath components constituting the channel within the Bayesian framework by locally maximizing the evidence integral. We show that, in the case of channel sounding using pulse-compression techniques, it is possible to cast the channel model as a general linear model, thus allowing RVM methods to be applied. We extend the original RVM algorithm to the multiple-observation/multiple-sensor scenario by proposing a new graphical model to represent multipath components. Through the analysis of the evidence procedure we develop a thresholding algorithm that is used in estimating the number of components. We also discuss the relationship of the evidence procedure to the standard minimum description length (MDL) criterion. We show that the maximum of the evidence corresponds to the minimum of the MDL criterion. The applicability of the proposed scheme is demonstrated with synthetic as well as real-world channel measurements, and a performance increase over the conventional MDL criterion applied to maximum-likelihood estimates of the channel parameters is observed

Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes

Information gathering (IG) algorithms aim to intelligently select a mobile sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for IG that employ Gaussian processes (GPs) as underlying model of the process. However, most algorithms discretize the state space, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they are not suited for online information gathering tasks as they assume prior knowledge about GP parameters. This paper presents a novel approach that tackles the two aforementioned issues. Specifically, our approach includes two intertwined steps: (i) a Rapidly-Exploring Random Tree (RRT) search that allows a robot to identify unvisited locations, and to learn the GP parameters, and (ii) an RRT*-based informative path planning that guides the robot towards those locations by maximizing the information gathered while minimizing path cost. The combination of the two steps allows an online realization of the algorithm, while eliminating the need for discretization. We demonstrate that our proposed algorithm outperforms state-of-the-art both in simulations, and in a lab experiment in which a ground-based robot explores the magnetic field intensity within an indoor environment populated with obstacles

Decentralized Multi-Agent Exploration with Online-Learning of Gaussian Processes

Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multiagent approaches offer well known advantages for exploration. Previous decentralized multi-agent exploration algorithms that use Gaussian processes as underlying data model, have only been validated through simulations. However, the implementation of an exploration algorithm brings difficulties that were not tackle yet. In this work, we propose an exploration algorithm that deals with the following challenges: (i) which information to transmit to achieve multi-agent coordination; (ii) how to implement a light-weight collision avoidance; (iii) how to learn the data’s model without prior information. We validate our algorithm with two experiments employing real robots. First, we explore the magnetic field intensity with a ground-based robot. Second, two quadcopters equipped with an ultrasound sensor explore a terrain profile. We show that our algorithm outperforms a meander and a random trajectory, as well as we are able to learn the data’s model online while exploring

Near-Surface Seismic Measurements in Gravel Pit, Over Highway Tunnel and Underground Tubes with Ground Truth Information as an Open Data Set

In this article, we describe in detail three seismic measurement campaigns based on refraction methods that we conducted at different sites in Bavaria, Germany. The measured data is published as an open data set. The particularity of this data set lies in its available ground truth information about each measurement site. Acquiring seismic data from sites with ground truth information is important for validation of seismic inversion algorithms. Since near-surface seismic field data with ground truth information is rather limited, we anticipate this data set to be a valuable contribution to the research community. For the measurements, three sites have been selected: (1) a gravel pit with a ground water layer, (2) a site above a highway tunnel and (3) a surface over underground tubes. The measurements have been conducted using line arrays of geophones, the Geode Seismograph from Geometrics Inc. and hammer strikes as seismic source. To obtain inversion results a travel time tomography based on first-arrivals within the software SeisImager is used. The inversion results show that we are able to image the ground water layer in the gravel pit, the highway tunnel and partly features of underground tubes. Furthermore, the results coincide with available ground truth information about the measurement sites. This paper summarizes the measurement campaigns and the respective data sets obtained through these campaigns. The data have been published by the authors as an open data set under the license CC BY 4.0 on figshare to make it available to the research community for validation of seismic data processing and inversion techniques

Swarm Technologies For Future Space Exploration Missions

Modern robotic platforms for in-situ space exploration are single-robots equipped with a number of specialized sensors providing scientists with unique information about a planet's surface. However, there is a number of exploration problems where large spatial apertures of the exploration system are necessary, requiring a completely new perspective on in-situ space exploration and it's required technologies. Large networks of robots, called swarm, pave the way: agents in a swarm span ad-hoc communication networks, localize themselves based on radio signals, share resources, process data and make inference over the network in a decentralized fashion. By cooperation, local information collected by agents becomes globally available. In this work we present our recent results in development of swarm technologies for future in-situ space exploration missions: a wireless system jointly used for communication and localization, and swarm navigation and exploration strategies to sample and reconstruct static spatial fields