Detection of UWB ranging measurement quality for collaborative indoor positioning

Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determine the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence, potentially achieves centimetre-level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging-based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning

Potentials of Deterministic Radio Propagation Simulation for AI-Enabled Localization and Sensing

Machine leaning (ML) and artificial intelligence (AI) enable new methods for localization and sensing in next-generation networks to fulfill a wide range of use cases. These approaches rely on learning approaches that require large amounts of training and validation data. This paper addresses the data generation bottleneck to develop and validate such methods by proposing an integrated toolchain based on deterministic channel modeling and radio propagation simulation. The toolchain is demonstrated exemplary for scenario classification to obtain localization-related channel parameters within an aircraft cabin environment

Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization

Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

3D Indoor Positioning in 5G networks

Over the past two decades, the challenge of accurately positioning objects or users indoors, especially in areas where Global Navigation Satellite Systems (GNSS) are not available, has been a significant focus for the research community. With the rise of 5G IoT networks, the quest for precise 3D positioning in various industries has driven researchers to explore various machine learning-based positioning techniques. Within this context, researchers are leveraging a mix of existing and emerging wireless communication technologies such as cellular, Wi-Fi, Bluetooth, Zigbee, Visible Light Communication (VLC), etc., as well as integrating any available useful data to enhance the speed and accuracy of indoor positioning. Methods for indoor positioning involve combining various parameters such as received signal strength (RSS), time of flight (TOF), time of arrival (TOA), time difference of arrival (TDOA), direction of arrival (DOA) and more. Among these, fingerprint-based positioning stands out as a popular technique in Real Time Localisation Systems (RTLS) due to its simplicity and cost-effectiveness. Positioning systems based on fingerprint maps or other relevant methods find applications in diverse scenarios, including malls for indoor navigation and geo-marketing, hospitals for monitoring patients, doctors, and critical equipment, logistics for asset tracking and optimising storage spaces, and homes for providing Ambient Assisted Living (AAL) services. A significant challenge facing all indoor positioning systems is the objective evaluation of their performance. This challenge is compounded by the coexistence of heterogeneous technologies and the rapid advancement of computation. There is a vast potential for information fusion to be explored. These observations have led to the motivation behind our work. As a result, two novel algorithms and a framework are introduced in this thesis

Rf sensing and processing methods for noninvasive health monitoring

Vulnerable populations include groups of people with a higher risk of poor health as a result of the limitations due to illness or disability. The health issues of vulnerable populations include three categories: physical, psychological, and social. The people with physical issues include high-risk mothers and infants, older adults and others with chronic illnesses and people with disabilities. The psychological issues of vulnerable populations include chronic mental conditions, such as bipolar disorder, major depression, and hyperactivity disorder, as well as substance abuse and those who are suicidal. The social issues in vulnerable populations include those living in abusive families, the homeless, etc. This dissertation concentrates on methods for helping two groups of vulnerable populations, namely, frail older adults and psychiatric hospital patients, to monitor their activity level, respiration rate, sleeping quality, and restless time in bed. In the first part of our work, we investigate a contactless monitoring system for psychiatric patients in a naturalistic hospital setting that can track their motion in bed, estimate the breathing rate of patients during their peaceful sleeping periods, and can be used to estimate a patient's restless time and sleep quality. Specifically, the contactless monitoring system uses a Vayyar Radar system with a carrier frequency of 6.014 GHz to capture all reflections by the FMCW (frequency modulation continuous waveform) signal. The Vayyar Radar system has been installed in a Psychiatric Center to capture 12 nights with over 135 hours of data from 7 patients. A depth camera and a thermal camera have also been installed and are used as the ground truth. The goal is to classify in bed and out of bed classes, quantify restlessness in bed, and determine the breathing rate while patients are lying in bed. We have simulated the psychiatric hospital set-up in the lab, where a respiration belt is used for ground truth, and tested the system with body postures of patients observed in the psychiatric hospital. We estimated respiration rate with different sleep postures, with the aim of investigating a contactless monitoring system for psychiatric patients in the hospital that can estimate the breathing rate of patients during typical sleeping postures, and find the torso area when the patients use other postures, such as reading books in bed or reversing the body on the bed. In the second part of our work, we investigate two methods for learning the room structure via radio wave reflections for longitudinal health monitoring of older adults in a naturalistic home setting. The goal is to use these data as part of a monitoring system that can be easily installed in a home with minimal configuration, for the purpose of detecting very early signs of illness and functional decline. Two studies are conducted using RF (radio frequency) sensing. The first method learns the structure from the RF clutter patterns and uses the beat frequency of the maximum peak in each chirp to calculate the wall position. The second method learns the room structure from active movement patterns and uses the open space between the clusters of active movement patterns to estimate the possible wall locations. Comparing the two results from these methods provides a more robust wall location. In addition, a background filter is designed based on the wall position, and the activity level of people in different rooms is estimated using a fuzzy rule system applied to the RF motion data

UWB sensor based indoor LOS/NLOS localization with support vector machine learning

Ultra-wideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-line-of-sight (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCP) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This paper derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed SVM based classification methodology leverages on the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27 %, and the LOS and NLOS recalls are 94.27 % and 92.57 %, respectively. The ranging errors in LOS(NLOS) conditions are reduced from 0.106 m(1.442 m) to 0.065 m(0.739 m), demonstrating an improvement of 38.85 %(48.74 %) with 0.041 m(0.703 m) error reduction. On the other hand, the average positioning accuracy is also reduced from 0.250 m to 0.091 m with an improvement of 63.49 %(0.159 m), which outperforms the state-of-the-art approaches of the Least-squares support vector machine (LS-SVM) and K-Nearest Neighbor (KNN) algorithms

Deteção e classificação de movimentos captados por radar para aplicações em casas inteligentes

Smart homes are homes equipped with advanced automation and control systems, encompassing a variety of functions and devices, such as lighting, security, entertainment and temperature regulation, with the aim of offering greater comfort to their occupants. In such a wide range of applications, the detection and classification of movement is of remarkable importance, allowing, for example, the adaptation of temperature based on the type of movement made by a person. This dissertation presents a system for detection and classification movements captured by radar for Smart Home applications. For this purpose, a frequency modulated continuous wave radar operating in the 77 - 81 GHz band is used, where it was possible to obtain the person location and movement velocity where the person was detected. The movements were classified using Machine Learning algorithms such as K - Nearest Neighbours, Support Vector Machine, Linear Discriminant Analysis and Random Forest. To this end, data was collected from 10 different people in order to obtain an extensive data set. The positioning of the radar in the division was also studied in order to have good data collection. The data was processed using Matlab, where characteristics were created to differentiate the movements to be classified, for example, the Doppler values or speed that a person has in different types of movement. The performance of each Machine Learning algorithm used was studied.As casas inteligentes são residências equipadas com sistemas avançados de automação e controlo, englobando uma variedade de funções e dispositivos, como iluminação, segurança, entretenimento e regulação de temperatura, com o propósito de oferecer maior conforto aos seus ocupantes. Num contexto tão vasto de aplicações, a deteção e classificação de movimento assumem uma relevância notável, permitindo, por exemplo, a adaptação da temperatura com base no tipo de movimento efetuado por uma pessoa. Nesta dissertação é apresentado um sistema para deteção e classificação de movimentos captados por radar para aplicações em casas inteligentes. Para o efeito é usado um Radar de onda contínua com frequência modulada, a operar na banda de 77 - 81 GHz, onde foi possível obter os valores da localização e da velocidade de movimento de uma pessoa onde a pessoa foi detetada. A classificação dos movimentos foi feita atráves de algoritmos de Machine Learning, tais como K - Nearest Neighbors, Support Vector Machine, Linear Discriminant Analysis and Random Forest. Para tal foi feita a recolha de dados de 10 pessoas diferentes de maneira a obter-se um extenso conjunto de dados. O posicionamento do Radar na divisão foi também estudado de maneira a ter-se uma boa recolha de dados. Com recurso ao Matlab, foi feito o processamento dos dados, onde foram criadas características para diferenciar os movimentos a classificar, por exemplo, os valores de Doppler ou velocidade que uma pessoa tem em diferentes tipos de movimento. A performance de cada algoritmo de Machine Learning utilizado é estudado.Mestrado em Engenharia Eletrónica e Telecomunicaçõe