5G Positioning and Mapping with Diffuse Multipath

5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters(delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods breakdown or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach

Indoor positioning with deep learning for mobile IoT systems

2022 Summer.Includes bibliographical references.The development of human-centric services with mobile devices in the era of the Internet of Things (IoT) has opened the possibility of merging indoor positioning technologies with various mobile applications to deliver stable and responsive indoor navigation and localization functionalities that can enhance user experience within increasingly complex indoor environments. But as GPS signals cannot easily penetrate modern building structures, it is challenging to build reliable indoor positioning systems (IPS). Currently, Wi-Fi sensing based indoor localization techniques are gaining in popularity as a means to build accurate IPS, benefiting from the prevalence of 802.11 family. Wi-Fi fingerprinting based indoor localization has shown remarkable performance over geometric mapping in complex indoor environments by taking advantage of pattern matching techniques. Today, the two main information extracted from Wi-Fi signals to form fingerprints are Received Signal Strength Index (RSSI) and Channel State Information (CSI) with Orthogonal Frequency-Division Multiplexing (OFDM) modulation, where the former can provide the average localization error around or under 10 meters but has low hardware and software requirements, while the latter has a higher chance to estimate locations with ultra-low distance errors but demands more resources from chipsets, firmware/software environments, etc. This thesis makes two novel contributions towards realizing viable IPS on mobile devices using RSSI and CSI information, and deep machine learning based fingerprinting. Due to the larger quantity of data and more sophisticated signal patterns to create fingerprints in complex indoor environments, conventional machine learning algorithms that need carefully engineered features suffer from the challenges of identifying features from very high dimensional data. Hence, the abilities of approximation functions generated from conventional machine learning models to estimate locations are limited. Deep machine learning based approaches can overcome these challenges to realize scalable feature pattern matching approaches such as fingerprinting. However, deep machine learning models generally require considerable memory footprint, and this creates a significant issue on resource-constrained devices such as mobile IoT devices, wearables, smartphones, etc. Developing efficient deep learning models is a critical factor to lower energy consumption for resource intensive mobile IoT devices and accelerate inference time. To address this issue, our first contribution proposes the CHISEL framework, which is a Wi-Fi RSSI- based IPS that incorporates data augmentation and compression-aware two-dimensional convolutional neural networks (2D CAECNNs) with different pruning and quantization options. The proposed model compression techniques help reduce model deployment overheads in the IPS. Unlike RSSI, CSI takes advantages of multipath signals to potentially help indoor localization algorithms achieve a higher level of localization accuracy. The compensations for magnitude attenuation and phase shifting during wireless propagation generate different patterns that can be utilized to define the uniqueness of different locations of signal reception. However, all prior work in this domain constrains the experimental space to relatively small-sized and rectangular rooms where the complexity of building interiors and dynamic noise from human activities, etc., are seldom considered. As part of our second contribution, we propose an end-to-end deep learning based framework called CSILoc for Wi-Fi CSI-based IPS on mobile IoT devices. The framework includes CSI data collection, clustering, denoising, calibration and classification, and is the first study to verify the feasibility to use CSI for floor level indoor localization with minimal knowledge of Wi-Fi access points (APs), thus avoiding security concerns during the offline data collection process

Characterization of Single- and Multi-antenna Wireless Channels

The wireless propagation channel significantly influences the received signal, so that it needs to be modeled effectively. Extensive measurements and analysis are required for investigating the validity of theoretical models and postulating new models based on measurements. Such measurements, analysis, and modeling are the topic of this thesis. The focus of the included contributions are Multiple-Input Multiple-Output (MIMO) propagation channels and radio channels for sensor network applications. Paper I presents results from one of the first MIMO measurements for a double-directional characterization of the outdoor-to-indoor wireless propagation channel. Such channels are of interest for both cellular and wireless LAN applications. We discuss physical aspects of building penetration, and also provide statistics of angle and delay spreads in the channel. The paper also investigates the coupling between DOD and DOA and the two spectra are found to have non-negligible dependence. We test the applicability of three analytical channel models that make different assumptions on the coupling between DODs and DOAs. Our results indicate that analytical models, that impose fewer restrictions on the DOD to DOA coupling, should be used preferrably over models such as the Kronecker model that have more restrictive assumptions. Paper II presents a cluster-based analysis of the outdoor-to-indoor MIMO measurements analyzed in Paper I. A subset of parameters of the COST 273 channel model, a generic model for MIMO propagation channels, are characterized for the outdoor-to-indoor scenario. MPC parameters are extracted at each measured location using a high-resolution algorithm and clusters of MPCs are identified with an automated clustering approach. In particular, the adopted clustering approach requires that all MPC parameters must be similar in order for the MPCs to form a cluster. A statistical analysis of the identified clusters is performed for both the intra- and inter-cluster properties. Paper III analyzes the spatial fading distribution for a range of canonical sensor deployment scenarios. The presented results are relevant to communicating within, and between, clusters of nodes. Contrary to the widely accepted assumption in published literature that the channel is AWGN at a small-enough distance, our measurements indicate that values of the Rice factor do not, in general, increase monotonically as the Tx-Rx distance is reduced. A probability mixture model is presented, with distance dependent parameters, to account for the distance dependent variations of the Rice factor. A simulation model that includes small- and large-scale fading effects is presented. According to the modeling approach, a sensor node placed anywhere within the spatial extent of a small-scale region will experience the channel statistics applicable to that region. Paper IV presents results characterizing a radio channel for outdoor short-range sensor networks. A number of antennas are placed on the ground in an open area and time-variation of the channel is induced by a person moving in the vicinity of the nodes. The channel statistics of both the LOS path and the overall narrowband signal are non-stationary. We investigate the stationarity interval length to be used for small-scale analysis. Our analysis of the various measured links shows that the Rx signal strength is significantly influenced by a moving person only when the person blocks the LOS path. We present a generic approach for modeling the LOS blockage, and also model the time-variant Doppler spectrum of the channel's scattered components

Localisation and tracking of people using distributed UWB sensors

In vielen Überwachungs- und Rettungsszenarien ist die Lokalisierung und Verfolgung von Personen in Innenräumen auf nichtkooperative Weise erforderlich. Für die Erkennung von Objekten durch Wände in kurzer bis mittlerer Entfernung, ist die Ultrabreitband (UWB) Radartechnologie aufgrund ihrer hohen zeitlichen Auflösung und Durchdringungsfähigkeit Erfolg versprechend. In dieser Arbeit wird ein Prozess vorgestellt, mit dem Personen in Innenräumen mittels UWB-Sensoren lokalisiert werden können. Er umfasst neben der Erfassung von Messdaten, Abstandschätzungen und dem Erkennen von Mehrfachzielen auch deren Ortung und Verfolgung. Aufgrund der schwachen Reflektion von Personen im Vergleich zum Rest der Umgebung, wird zur Personenerkennung zuerst eine Hintergrundsubtraktionsmethode verwendet. Danach wird eine konstante Falschalarmrate Methode zur Detektion und Abstandschätzung von Personen angewendet. Für Mehrfachziellokalisierung mit einem UWB-Sensor wird eine Assoziationsmethode entwickelt, um die Schätzungen des Zielabstandes den richtigen Zielen zuzuordnen. In Szenarien mit mehreren Zielen kann es vorkommen, dass ein näher zum Sensor positioniertes Ziel ein anderes abschattet. Ein Konzept für ein verteiltes UWB-Sensornetzwerk wird vorgestellt, in dem sich das Sichtfeld des Systems durch die Verwendung mehrerer Sensoren mit unterschiedlichen Blickfeldern erweitert lässt. Hierbei wurde ein Prototyp entwickelt, der durch Fusion von Sensordaten die Verfolgung von Mehrfachzielen in Echtzeit ermöglicht. Dabei spielen insbesondere auch Synchronisierungs- und Kooperationsaspekte eine entscheidende Rolle. Sensordaten können durch Zeitversatz und systematische Fehler gestört sein. Falschmessungen und Rauschen in den Messungen beeinflussen die Genauigkeit der Schätzergebnisse. Weitere Erkenntnisse über die Zielzustände können durch die Nutzung zeitlicher Informationen gewonnen werden. Ein Mehrfachzielverfolgungssystem wird auf der Grundlage des Wahrscheinlichkeitshypothesenfilters (Probability Hypothesis Density Filter) entwickelt, und die Unterschiede in der Systemleistung werden bezüglich der von den Sensoren ausgegebene Informationen, d.h. die Fusion von Ortungsinformationen und die Fusion von Abstandsinformationen, untersucht. Die Information, dass ein Ziel detektiert werden sollte, wenn es aufgrund von Abschattungen durch andere Ziele im Szenario nicht erkannt wurde, wird als dynamische Überdeckungswahrscheinlichkeit beschrieben. Die dynamische Überdeckungswahrscheinlichkeit wird in das Verfolgungssystem integriert, wodurch weniger Sensoren verwendet werden können, während gleichzeitig die Performanz des Schätzers in diesem Szenario verbessert wird. Bei der Methodenauswahl und -entwicklung wurde die Anforderung einer Echtzeitanwendung bei unbekannten Szenarien berücksichtigt. Jeder untersuchte Aspekt der Mehrpersonenlokalisierung wurde im Rahmen dieser Arbeit mit Hilfe von Simulationen und Messungen in einer realistischen Umgebung mit UWB Sensoren verifiziert.Indoor localisation and tracking of people in non-cooperative manner is important in many surveillance and rescue applications. Ultra wideband (UWB) radar technology is promising for through-wall detection of objects in short to medium distances due to its high temporal resolution and penetration capability. This thesis tackles the problem of localisation of people in indoor scenarios using UWB sensors. It follows the process from measurement acquisition, multiple target detection and range estimation to multiple target localisation and tracking. Due to the weak reflection of people compared to the rest of the environment, a background subtraction method is initially used for the detection of people. Subsequently, a constant false alarm rate method is applied for detection and range estimation of multiple persons. For multiple target localisation using a single UWB sensor, an association method is developed to assign target range estimates to the correct targets. In the presence of multiple targets it can happen that targets closer to the sensor induce shadowing over the environment hindering the detection of other targets. A concept for a distributed UWB sensor network is presented aiming at extending the field of view of the system by using several sensors with different fields of view. A real-time operational prototype has been developed taking into consideration sensor cooperation and synchronisation aspects, as well as fusion of the information provided by all sensors. Sensor data may be erroneous due to sensor bias and time offset. Incorrect measurements and measurement noise influence the accuracy of the estimation results. Additional insight of the targets states can be gained by exploiting temporal information. A multiple person tracking framework is developed based on the probability hypothesis density filter, and the differences in system performance are highlighted with respect to the information provided by the sensors i.e. location information fusion vs range information fusion. The information that a target should have been detected when it is not due to shadowing induced by other targets is described as dynamic occlusion probability. The dynamic occlusion probability is incorporated into the tracking framework, allowing fewer sensors to be used while improving the tracker performance in the scenario. The method selection and development has taken into consideration real-time application requirements for unknown scenarios at every step. Each investigated aspect of multiple person localization within the scope of this thesis has been verified using simulations and measurements in a realistic environment using M-sequence UWB sensors

Dynamic Channel Modeling for Indoor Millimeter-Wave Propagation Channels Based on Measurements

In this contribution, a recently conducted measurement campaign for indoor millimeter-wave propagation channels is introduced. A vector network analyzer (VNA)-based channel sounder was exploited to record the channel characteristics at the frequency band from 28-30 GHz. A virtual uniform circular array (UCA) with a radius of 0.25m was formed using a rotator with 360 steps. Moreover, by taking advantage of fiber-optic technique applied in the channel sounder, measurements at 50 positions were performed from an indoor hall to an indoor corridor along a long pre-defined route. A low-complexity highresolution propagation estimation (HRPE) algorithm is exploited to estimate the propagation parameters of multipath components (MPCs). Based on the HRPE estimation results, a novel clustering identification and tracking algorithm is proposed to trace clusters. Composite channel characteristics, cluster-level characteristics and dynamic (or birth-death) behaviours of the clusters are investigated, which constitute a dynamic model for the indoor millimeter-wave channel

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

Opportunistic timing signals for pervasive mobile localization

Mención Internacional en el título de doctorThe proliferation of handheld devices and the pressing need of location-based services call for precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices. Despite the large investments and efforts in academic and industrial communities, a pin-point solution is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban areas and indoor environments, where the visibility of GNSS satellites is reduced drastically. In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive positioning system should still rely on GNSS, yet complemented with technologies that can guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals are made of data with timing information. We then investigate solutions where opportunistic timing signals can be extracted out of terrestrial technologies. These signals can then be used as additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system that combines range measurements from the Global Positioning System (GPS), the world’s most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques have been recently suggested for ranging mobile devices overWiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments, and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters. The next step we envisioned towards preparing theoretical and practical basis for the aforementioned hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF ranges, and initial foundations of single-technology self-localization. Self-localization systems based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. We study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings. These tools and foundations are necessary to tackle the problem of hybrid positioning system providing high localization capabilities across indoor and outdoor environments. In this context, the lack of a single positioning system that is able the fulfill the specific requirements of diverse indoor and outdoor applications settings has led the development of a multitude of localization technologies. Existing mobile devices such as smartphones therefore commonly rely on a multi-RAT (Radio Access Technology) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is foreseen to provide the highest accuracy in the particular context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies and improve the overall localization accuracy in different contexts. We introduce an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a rich set of multiple ranges can be available across different RATs, the intelligent selection of the subset of ranges with accurate timing information is critical to achieve the best positioning accuracy. We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging measurements. We also address practical problems of the design space, such as removal of WiFi chipset and environmental calibration to make the positioning system as autonomous as possible. Experimental results show that our solution considerably outperforms the use of monolithic technologies and methods based on classical fault detection and identification typically applied in standalone GPS technology. All the contributions and research questions described previously in localization and positioning related topics suppose full knowledge of the anchors positions. In the last part of this work, we study the problem of deriving proximity metrics without any prior knowledge of the positions of the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and respective received signal strength indicator (RSSI) values. Applications that benefit from proximity metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors, we show that metrics from related work perform poorly on real-world data. We analyze the cause for this poor performance, and show that imperfect observations of APs with commodity WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide such proximity estimates, without requiring knowledge of location for the observed AP. We address the challenge of imperfect observations of APs in the design of these improved metrics. Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints. We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en Ingeniería TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing