Cooperative Estimation of Maps of Physical and Virtual Radio Transmitters

In multipath assisted positioning, the spatial information contained in multipath components (MPCs) is exploited, as MPCs are regarded as line-of-sight signals from virtual transmitters. The positions of physical and virtual transmitters can be estimated jointly with the receiver position with simultaneous localization and mapping (SLAM). In our multipath assisted positioning approach called Channel-SLAM, the estimates from a channel estimator are used in a Rao-Blackwellized particle filter which implements SLAM. While the original Channel-SLAM algorithm is a single-user positioning system, we present a comprehensive framework for cooperative Channel-SLAM within this paper. Users cooperate by exchanging maps of estimated transmitter locations. With prior information about the locations of physical and virtual transmitters, the positioning performance of the users increases significantly. The more users contribute to such a transmitter map, the more increases the positioning performance. With simulations in an indoor scenario, we show that the positioning performance is bounded for cooperative Channel-SLAM in the long run

Multipath Assisted Positioning with Transmitter Visibility Information

In multipath assisted positioning, multipath components (MPCs) are regarded as line-of-sight (LoS) signals from virtual transmitters. Instead of trying to mitigate the influence of MPCs, the spatial information contained in MPCs is exploited for localization. The locations of the physical and virtual transmitters are in general unknown but can be estimated with simultaneous localization and mapping (SLAM). Recently, a multipath assisted positioning algorithm named Channel-SLAM for terrestrial radio signals has been introduced. It simultaneously tracks the position of a receiver and maps the locations of physical and virtual radio transmitters. Maps of estimated transmitter locations can be augmented by additional information. Within this paper, we propose to extend the Channel-SLAM algorithm by mapping information about the visibility of transmitters. A physical or virtual transmitter is visible, if its signal is received in a LoS condition. We derive a novel particle filter for Channel-SLAM that estimates and exploits visibility information on transmitters in addition to their locations. We show by means of simulations in an indoor scenario that our novel particle filter improves the positioning performance of Channel-SLAM considerably

Beyond the spatio-temporal limits of atmospheric radars: inverse problem techniques and MIMO systems

The Earth’s upper atmosphere (UA) is a highly dynamic region dominated by atmospheric waves and stratified turbulence covering a wide range of spatio-temporal scales. A comprehensive study of the UA requires measurements over a broad range of frequencies and spatial wavelengths, which are prohibitively costly. To improve the understanding of the UA, an investment in efficient and large observational infrastructures is required. This work investigates remote sensing techniques based on MIMO and inverse problems techniques to improve the capabilities of current atmospheric radars

Geometry-based Radio Channel Characterization and Modeling: Parameterization, Implementation and Validation

The propagation channel determines the fundamental basis of wireless communications, as well as the actual performance of practical systems. Therefore, having good channel models is a prerequisite for developing the next generation wireless systems. This thesis first investigates one of the main channel model building blocks, namely clusters. To understand the concept of clusters and channel characterization precisely, a measurement based ray launching tool has been implemented (Paper I). Clusters and their physical interpretation are studied by using the implemented ray launching tool (Paper II). Also, this thesis studies the COST 2100 channel model, which is a geometry-based channel model using the concept of clusters. A complete parameter set for the outdoor sub-urban scenario is extracted and validated for the COST 2100 channel model (Paper III). This thesis offers valuable insights on multi-link channel modeling, where it will be widely used in the next generation wireless systems (Paper IV and Paper V). In addition, positioning and localization by using the phase information of multi-path components, which are estimated and tracked from the radio channels, are investigated in this thesis (Paper VI). Clusters are extensively used in geometry-based stochastic channel models, such as the COST 2100 and WINNER II channel models. In order to gain a better understanding of the properties of clusters, thus the characteristics of wireless channels, a measurement based ray launching tool has been implemented for outdoor scenarios in Paper I. With this ray launching tool, we visualize the most likely propagation paths together with the measured channel and a detail floor plan of the measured environment. The measurement based ray launching tool offers valuable insights of the interacting physical scatterers of the propagation paths and provides a good interpretation of propagation paths. It shows significant advantages for further channel analysis and modeling, e.g., multi-link channel modeling. \par The properties of clusters depend on how clusters are identified. Generally speaking, there are two kinds of clusters: parameter based clusters are characterized with the parameters of the associated multi-path components; physical clusters are determined based on the interacting physical scatterers of the multi-path components. It is still an open issue on how the physical clusters behave compared to the parameter based clusters and therefore we analyze this in more detail in Paper II. In addition, based on the concept of physical clusters, we extract modeling parameters for the COST 2100 channel model with sub-urban and urban micro-cell measurements. Further, we validate these parameters with the current COST 2100 channel model MATLAB implementation. The COST 2100 channel model is one of the best candidates for the next generation wireless systems. Researchers have made efforts to extract the parameters in an indoor scenario, but the parameterization of outdoor scenarios is missing. Paper III fills this blank, where, first, cluster parameters and cluster time-variant properties are obtained from the 300~MHz measurements by using a joint clustering and tracking algorithm. Parameterization of the COST 2100 channel model for single-link outdoor MIMO communication at 300~MHz is conducted in Paper III. In addition, validation of the channel model is performed for the considered scenario by comparing simulated and measured delay spreads, spatial correlations, singular value distributions and antenna correlations. Channel modeling for multi-link MIMO systems plays an important role for the developing of the next generation wireless systems. In general, it is essential to capture the correlations between multi-link as well as their correlation statistics. In Paper IV, correlation between large-scale parameters for a macro cell scenario at 2.6 GHz has been analyzed. It has been found that the parameters of different links can be correlated even if the base stations are far away from each other. When both base stations were in the same direction compared to the movement, the large-scale parameters of the different links had a tendency to be positively correlated, but slightly negatively correlated when the base stations were located in different directions compared to the movement of the mobile terminal. Paper IV focuses more on multi-site investigations, and paper V gives valuable insights for multi-user scenarios. In the COST 2100 channel model, common clusters are proposed for multi-link channel modeling. Therefore, shared scatterers among the different links are investigated in paper V, which reflects the physical existence of common clusters. We observe that, as the MS separation distance is increasing, the number of common clusters is decreasing and the cross-correlation between multiple links is decreasing as well. Multi-link MIMO simulations are also performed using the COST 2100 channel model and the parameters of the extracted common clusters are detailed in paper V. It has been demonstrated that the common clusters can represent multi-link properties well with respect to inter-link correlation and sum rate capacity. Positioning has attracted a lot of attention both in the industry and academia during the past decades. In Paper VI, positioning with accuracy down to centimeters has been demonstrated, where the phase information of multi-path components from the measured channels is used. First of all, an extended Kalman filter is implemented to process the channel data, and the phases of a number of MPCs are tracked. The tracked phases are converted into relative distance measures. Position estimates are obtained with a method based on so called structure-of-motion. In Paper VI, circular movements have been successfully tracked with a root-mean-square error around 4 cm when using a bandwidth of 40 MHz. It has been demonstrated that phase based positioning is a promising technique for positioning with accuracy down to centimeters when using a standard cellular bandwidth. In summary, this thesis has made efforts for the implementation of the COST 2100 channel model, including providing model parameters and validating such parameters, investigating multi-link channel properties, and suggesting implementations of the channel model. The thesis also has made contributions to the tools and algorithms that can be used for general channel characterizations, i.e., clustering algorithm, ray launching tool, EKF algorithm. In addition, this thesis work is the first to propose a practical positioning method by utilizing the distance estimated from the phases of the tracked multi-path components and showed a preliminary and promising result

Mixture Density Networks for Multipath Assisted Positioning-based Fingerprinting

In multipath assisted positioning schemes, the spatial information contained in multipath propagation of wireless radio systems is exploited for localization of a receiver. However, such schemes suffer from a high computational complexity. We have proposed before a fingerprinting localization system based on multipath assisted positioning, where the fingerprinting database is encoded in a deep neural network (DNN). Within this paper, we propose and evaluate a mixture density network approach in our DNN to analyze ambiguities among fingerprints at different locations. We show that our scheme shows a very good positioning performance with an error of around 2m for the most part, while having a low computational complexity in the online stage and a very low effort compared to traditional fingerprinting schemes

Fingerprint location methods using ray-tracing

Mobile location methods that employ signal fingerprints are becoming increasingly popular in a number of wireless positioning solutions. A fingerprint is a spatial database, created either by recorded measurement or simulation, of the radio environment. It is used to assign signal characteristics such as received signal strength or power delay profiles to an actual location. Measurements made by either the handset or the network, are then matched to those in the fingerprint in order to determine a location. Creation of the fingerprint by an a priori measurement stage is costly and time consuming. Virtual fingerprints, those created by a ray-tracing radio propagation prediction tool, normally require a lengthy off-line simulation mode that needs to be repeated each time changes are made to the network or built environment. An open research question exists of whether a virtual fingerprint could be created dynamically via a ray-trace model embedded on a mobile handset for positioning purposes. The key aim of this thesis is to investigate the trade-off between complexity of the physics required for ray-tracing models and the accuracy of the virtual fingerprints they produce. The most demanding computational phase of a ray-trace simulation is the ray-path finding stage, whereby a distribution of rays cast from a source point, interacting with walls and edges by reflection and diffraction phenomena are traced to a set of receive points. Due to this, we specifically develop a new technique that decreases the computation of the ray-path finding stage. The new technique utilises a modified method of images rather than brute-force ray casting. It leads to the creation of virtual fingerprints requiring significantly less computation effort relative to ray casting techniques, with only small decreases in accuracy. Our new technique for virtual fingerprint creation was then applied to the development of a signal strength fingerprint for a 3G UMTS network covering the Sydney central business district. Our main goal was to determine whether on current mobile handsets, a sub-50m location accuracy could be achieved within a few seconds timescale using our system. The results show that this was in fact achievable. We also show how virtual fingerprinting can lead to more accurate solutions. Based on these results we claim user embedded fingerprinting is now a viable alternative to a priori measurement schemes

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Vision Sensors and Edge Detection

Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing