Distance-based sensor node localization by using ultrasound, RSSI and ultra-wideband - A comparision between the techniques

Wireless sensor networks (WSNs) have become one of the most important topics in wireless communication during the last decade. In a wireless sensor system, sensors are spread over a region to build a sensor network and the sensors in a region co-operate to each other to sense, process, filter and routing. Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. WSNs have so many applications in different areas such as health-care, monitoring and control, rescuing and military; they all depend on nodes being able to accurately determine their locations. This master’s thesis is focused on distance-based sensor node localization techniques; Received signal strength indicator, ultrasound and ultra-wideband. Characteristics and factors which affect these distance estimation techniques are analyzed theoretically and through simulation the quality of these techniques are compared in different scenarios. MDS, a centralized algorithm is used for solving the coordinates. It is a set of data analysis techniques that display the structure of distance-like data as a geometrical picture. Centralized and distributed implementations of MDS are also discussed. All simulations and computations in this thesis are done in Matlab. Virtual WSN is simulated on Sensorviz. Sensorviz is a simulation and visualization tool written by Andreas Savvides.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Distance-based sensor node localization by using ultrasound, RSSI and ultra-wideband - A comparision between the techniques

Wireless sensor networks (WSNs) have become one of the most important topics in wireless communication during the last decade. In a wireless sensor system, sensors are spread over a region to build a sensor network and the sensors in a region co-operate to each other to sense, process, filter and routing. Sensor Positioning is a fundamental and crucial issue for sensor network operation and management. WSNs have so many applications in different areas such as health-care, monitoring and control, rescuing and military; they all depend on nodes being able to accurately determine their locations. This master’s thesis is focused on distance-based sensor node localization techniques; Received signal strength indicator, ultrasound and ultra-wideband. Characteristics and factors which affect these distance estimation techniques are analyzed theoretically and through simulation the quality of these techniques are compared in different scenarios. MDS, a centralized algorithm is used for solving the coordinates. It is a set of data analysis techniques that display the structure of distance-like data as a geometrical picture. Centralized and distributed implementations of MDS are also discussed. All simulations and computations in this thesis are done in Matlab. Virtual WSN is simulated on Sensorviz. Sensorviz is a simulation and visualization tool written by Andreas Savvides.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

A Survey of Positioning Systems Using Visible LED Lights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

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

mmWave V2V Localization in MU-MIMO Hybrid Beamforming

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in a number of vehicles reduces the Cramr-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters

MmWave V2V Localization in MU-MIMO Hybrid Beamforming

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in a number of vehicles reduces the Cramr-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters

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

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