Improving the performance of a radio-frequency localization system in adverse outdoor applications

In outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate's performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm

Exploiting Structural Signal Information in Passive Emitter Localization

The operational use of systems for passive geolocation of radio frequency emitters poses various challenges to single sensor systems or sensor networks depending on the measurement methods. Position estimation by means of direction finding systems often requires complex receiver and antenna technique. Time (Difference) of Arrival methods (TDOA, TOA) are based on measurements regarding the signal propagation duration and generally require broadband communication links to transmit raw signal data between spatially separated receivers of a sensor network. Such bandwidth requirements are particularly challenging for applications with moving sensor nodes. This issue is addressed in this thesis and techniques that use signal structure information of the considered signals are presented which allow a drastic reduction of the communication requirements. The advantages of using knowledge of the signal structure for TDOA based emitter localization are shown using two exemplary applications. The first case example deals with the passive surveillance of the civil airspace (Air Traffic Management, ATM) using a stationary sensor network. State of the art airspace surveillance is mainly based on active radar systems (Primary Surveillance Radar, PSR), cooperative secondary radar systems (Secondary Surveillance Radar, SSR) and automatic position reports from the aircraft itself (Automatic Dependent Surveillance-Broadcast, ADS-B). SSR as well as ADS-B relies on aircrafts sending transponder signals at a center frequency of 1090 MHz. The reliability and accuracy of the position reports sent by aircrafts using ADS-B are limited and not sufficient to ensure safe airspace separation for example of two aircrafts landing on parallel runways. In the worst case, the data may even be altered with malicious intent. Using passive emitter localization and tracking based on multilateration (TDOA/hyperbolic localization), a precise situational awareness can be given which is independent of the content of the emitted transponder signals. The high concentration of sending targets and the high number of signals require special signal processing and information fusion techniques to overcome the huge amount of data. It will be shown that a multilateration network that employs those techniques can be used to improve airspace security at reasonable costs. For the second case, a concept is introduced which allows TDOA based emitter localization with only one moving observer platform. Conventional TDOA measurements are obtained using spatially distributed sensor nodes which capture an emitted signal at the same time. From those signals, the time difference of arrival is estimated. Under certain conditions, the exploitation of signal structure information allows to transfer the otherwise only spatial into a spatial and temporal measurement problem. This way, it is possible to obtain TDOA estimates over multiple measurement time steps using a single moving observer and to thus localize the emitter of the signals. The concept of direct position determination is applied to the single sensor signal structure TDOA scheme and techniques for direct single sensor TDOA are introduced. The validity and performance of the presented methods is shown in theoretical analysis in terms of Cramér-Rao Lower Bounds, Monte-Carlo simulations and by evaluation of real data gained during field experiments

Opportunistic Angle of Arrival Estimation in Impaired Scenarios

This work if focused on the analysis and the development of Angle of Arrival (AoA) radio localization methods. The radio positioning system considered is constituted by a radio source and by a receiving array of antennas. The positioning algorithms treated in this work are designed to have a passive and opportunistic approach. The opportunistic attribute implies that the radio localization algorithms are designed to provide the AoA estimation with nearly-zero information on the transmitted signals. No training sequences or waveforms custom designed for localization are taken into account. The localization is termed passive since there is no collaboration between the transmitter and the receiver during the localization process. Then, the algorithms treated in this work are designed to eavesdrop already existing communication signals and to locate their radio source with nearly-zero knowledge of the signal and without the collaboration of the transmitting node. First of all, AoA radio localization algorithms can be classified in terms of involved signals (narrowband or broadband), antenna array pattern (L-shaped, circular, etc.), signal structure (sinusoidal, training sequences, etc.), Differential Time of Arrival (D-ToA) / Differential Phase of Arrival (D-PoA) and collaborative/non collaborative. Than, the most detrimental effects for radio communications are treated: the multipath (MP) channels and the impaired hardware. A geometric model for the MP is analysed and implemented to test the robustness of the proposed methods. The effects of MP on the received signals statistics from the AoA estimation point-of-view are discussed. The hardware impairments for the most common components are introduced and their effects in the AoA estimation process are analysed. Two novel algorithms that exploits the AoA from signal snapshots acquired sequentially with a time division approach are presented. The acquired signals are QAM waveforms eavesdropped from a pre-existing communication. The proposed methods, namely Constellation Statistical Pattern IDentification and Overlap (CSP-IDO) and Bidimensional CSP-IDO (BCID), exploit the probability density function (pdf) of the received signals to obtain the D-PoA. Both CSP-IDO and BCID use the statistical pattern of received signals exploiting the transmitter statistical signature. Since the presence of hardware impairments modify the statistical pattern of the received signals, CSP-IDO and BCID are able to exploit it to improve the performance with respect to (w.r.t.) the ideal case. Since the proposed methods can be used with a switched antenna architecture they are implementable with a reduced hardware contrariwise to synchronous methods like MUltiple SIgnal Classification (MUSIC) that are not applicable. Then, two iterative AoA estimation algorithms for the dynamic tracking of moving radio sources are implemented. Statistical methods, namely PF, are used to implement the iterative tracking of the AoA from D-PoA measures in two different scenarios: automotive and Unmanned Aerial Vehicle (UAV). The AoA tracking of an electric car signalling with a IEEE 802.11p-like standard is implemented using a test-bed and real measures elaborated with a the proposed Particle Swarm Adaptive Scattering (PSAS) algorithm. The tracking of a UAV moving in the 3D space is investigated emulating the UAV trajectory using the proposed Confined Area Random Aerial Trajectory Emulator (CARATE) algorithm

3D indoor positioning of UAVs with spread spectrum ultrasound and time-of-flight cameras

Este trabajo propone el uso de un sistema híbrido de posicionamiento acústico y óptico en interiores para el posicionamiento 3D preciso de los vehículos aéreos no tripulados (UAV). El módulo acústico de este sistema se basa en un esquema de Acceso Múltiple por División de Código de Tiempo (T-CDMA), en el que la emisión secuencial de cinco códigos ultrasónicos de espectro amplio se realiza para calcular la posición horizontal del vehículo siguiendo un procedimiento de multilateración 2D. El módulo óptico se basa en una cámara de Tiempo de Vuelo (TOF) que proporciona una estimación inicial de la altura del vehículo. A continuación se propone un algoritmo recursivo programado en un ordenador externo para refinar la posición estimada. Los resultados experimentales muestran que el sistema propuesto puede aumentar la precisión de un sistema exclusivamente acústico en un 70-80% en términos de error cuadrático medio de posicionamiento.This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential emission of five spread spectrum ultrasonic codes is performed to compute the horizontal vehicle position following a 2D multilateration procedure. The optical module is based on a Time-Of-Flight (TOF) camera that provides an initial estimation for the vehicle height. A recursive algorithm programmed on an external computer is then proposed to refine the estimated position. Experimental results show that the proposed system can increase the accuracy of a solely acoustic system by 70–80% in terms of positioning mean square error.• Gobierno de España y Fondos para el Desarrollo Regional Europeo. Proyectos TARSIUS (TIN2015-71564-C4-4-R) (I+D+i), REPNIN (TEC2015-71426-REDT) y SOC-PLC (TEC2015-64835-C3-2-R) (I+D+i) • Junta de Extremadura, Fondos FEDER y Fondo Social Europeo. Proyecto GR15167 y beca predoctoral 45/2016 Exp. PD16030peerReviewe

Etude et réalisation d'un système de communications par lumière visible (VLC/LiFi). Application au domaine automobile.

The scientific problematic of this PhD is centered on the usage of Visible LightCommunications (VLC) in automotive applications. By enabling wireless communication amongvehicles and also with the traffic infrastructure, the safety and efficiency of the transportation canbe substantially increased. Considering the numerous advantages of the VLC technologyencouraged the study of its appropriateness for the envisioned automotive applications, as analternative and/or a complement for the traditional radio frequency based communications.In order to conduct this research, a low-cost VLC system for automotive application wasdeveloped. The proposed system aims to ensure a highly robust communication between a LEDbasedVLC emitter and an on-vehicle VLC receiver. For the study of vehicle to vehicle (V2V)communication, the emitter was developed based on a vehicle backlight whereas for the study ofinfrastructure to vehicle (I2V) communication, the emitter was developed based on a traffic light.Considering the VLC receiver, a central problem in this area is the design of a suitable sensorable to enhance the conditioning of the signal and to avoid disturbances due to the environmentalconditions, issues that are addressed in the thesis. The performances of a cooperative drivingsystem integrating the two components were evaluated as well.The experimental validation of the VLC system was performed in various conditions andscenarios. The results confirmed the performances of the proposed system and demonstrated thatVLC can be a viable technology for the considered applications. Furthermore, the results areencouraging towards the continuations of the work in this domain.La problématique scientifique de cette thèse est centrée sur le développement decommunications par lumière visible (Visible Light Communications - VLC) dans lesapplications automobiles. En permettant la communication sans fil entre les véhicules, ou entreles véhicules et l’infrastructure routière, la sécurité et l'efficacité du transport peuvent êtreconsidérablement améliorées. Compte tenu des nombreux avantages de la technologie VLC,cette solution se présente comme une excellente alternative ou un complément pour lescommunications actuelles plutôt basées sur les technologies radio-fréquences traditionnelles.Pour réaliser ces travaux de recherche, un système VLC à faible coût pour applicationautomobile a été développé. Le système proposé vise à assurer une communication très robusteentre un émetteur VLC à base de LED et un récepteur VLC monté sur un véhicule. Pour l'étudedes communications véhicule à véhicule (V2V), l'émetteur a été développé sur la base d’un pharearrière rouge de voiture, tandis que pour l'étude des communications de l'infrastructure auvéhicule (I2V), l'émetteur a été développé sur la base d'un feu de circulation. Considérant lerécepteur VLC, le problème principal réside autour d’un capteur approprié, en mesured'améliorer le conditionnement du signal et de limiter les perturbations dues des conditionsenvironnementales. Ces différents points sont abordés dans la thèse, d’un point de vue simulationmais également réalisation du prototype.La validation expérimentale du système VLC a été réalisée dans différentes conditions etscénarii. Les résultats démontrent que la VLC peut être une technologie viable pour lesapplications envisagées

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Viability and Performance of RF Source Localization Using Autocorrelation-Based Fingerprinting

Finding the source location of a radio-frequency (RF) transmission is a useful capability for many civilian, industrial, and military applications. This problem is particularly challenging when done “Blind,” or when the transmitter was not designed with finding its location in mind, and relatively little information is available about the signal before-hand. Typical methods for this operation utilize the time, phase, power, and frequency viewable from received signals. These features are all less predictable in indoor and urban environments, where signals undergo transformation from multiple interactions with the environment. These interactions imprint structure onto the received signal which is dependent on the transmission path, and therefore the initial location. Using a received signal, a signal characteristic known as the autocorrelation can be computed which will largely be shaped by this information. In this research, RF source localization using finger-printing (a technique involving matching to a known database) with signal autocorrelations is explored. A Gaussian-process-based method for autocorrelation based fingerprinting is proposed. Performance of this method is evaluated using a ray-tracing-based simulation of an indoor environment

Towards joint communication and sensing (Chapter 4)

Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed