Research team
Expertise
The activities of the space track I am leading can be summarized in 3 research pillars: 1. Communication: Research on hybrid terrestrial and non-terrestrial networks (TN/NTN), such as combining 5G/6G/LPWAN with satellite communication. 2. Navigation: Research on Positioning, Navigation and Timing with Low Earth Orbit satellites (LEO-PNT), specifically focusing on indoor and energy-efficient LEO-PNT. 3. Earth Observation: Research on resource-efficient, near-sensor AI algorithms onboard satellites to reduce the downlink in e.g. hyperspectral imaging (HSI) applications.
Indoor Localization with Low Earth Orbit Satellites.
Abstract
Positioning, Navigation and Timing (PNT) has become increasingly important in enabling many Location Based Services (LBS). Undoubtedly, the most used PNT systems are the Global Navigation Satellite Systems (GNSS), which provide worldwide coverage. Although GNSS has improved significantly over the past decades, multiple shortcomings are inherent to the design of the satellite system. The poor indoor reception and the susceptibility to jamming are a clear illustration of this. Therefore, a novel Low Earth Orbit (LEO) PNT system could provide a solution to these problems. Because these satellites are approximately 20 times closer to Earth, a significant signal strength difference can be observed. However, the topic of indoor localization with LEO satellites remains largely unexplored. To construct a new PNT constellation with good indoor accuracy and coverage the dynamic LEO satellite-to-indoor channel should be taken into account, together with optimizing carrier frequency, modulation, algorithms, and coding techniques within the many constraints. In this work, I quantify the LEO satellite-to-indoor channel characteristics. To achieve this, I will conduct numerous real-world measurements and quantify multipath and signal strength in a variety of indoor environments. This will be achieved by leveraging real LEO-PNT signals provided by ESA. Furthermore, a LEO satellite simulation will be constructed to analyze and optimize the PNT performance for indoor environments.Researcher(s)
- Promoter: Weyn Maarten
- Co-promoter: Janssen Thomas
- Fellow: Van Uytsel Wout
Research team(s)
Project type(s)
- Research Project
Reliable error estimation of signal feature-based localization in LPWAN.
Abstract
In recent years, Low Power Wide Area Networks (LPWAN) have received much attention, due to the rise of the Internet of Things (IoT) and the need to localize devices in these long-range networks, using minimal power consumption. Asset tracking is one of the classic applications of LPWAN localization. However, the more accurate a localization algorithm, the more application potential (e.g. home automation, health care solutions and smart cities) there is to use this algorithm. Therefore, we need advanced technologies and algorithms to improve the accuracy and reliability of LPWAN localization. Although feature-based localization is widely used in indoor environments, we will extend the use of this methodology to outdoor environments. Features are defined as signal characteristics, such as signal strength. The class of feature-based localization can be subdivided into different subclasses. Fingerprinting and ranging are two of the most important techniques in the featurebased class. In this research, we will investigate new and existing algorithms to increase the accuracy and reliability of feature-based localization techniques in LPWAN. A comparative study between the accuracy and reliability of LPWAN technologies (Sigfox, LoRaWAN and NB-IoT) will be made as well.Researcher(s)
- Promoter: Weyn Maarten
- Co-promoter: Berkvens Rafael
- Fellow: Janssen Thomas
Research team(s)
Project type(s)
- Research Project