Real-time vehicle monitoring and positioning using MQTT for reliable wireless connectivity

This project was aimed at developing affordable solutions to improve performances of real-time monitoring of road vehicles utilizing low-cost satellite positioning equipment and wireless cellular communication network. This study investigates the problems by measuring and benchmarking two-way communication latency, data reliability and positioning accuracy using low-cost GPS equipment and a lightweight machine-to-machine (M2M) protocol called Message Queue Transmission Telemetry (MQTT). A prototype of vehicle monitoring platform which consists of on-board-unit and central monitoring server was developed to facilitate the research study

Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems

The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage. B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Methods for Improving Performance in Consumer Grade GNSS Receivers

Viimeisten kolmen vuosikymmenen aikana satelliittinavigointi on kehittynyt ammatti ja sotilaskäyttäjien tekniikasta kaikkien saatavilla olevaksi tekniikaksi. Varsinkin viimeisen 15 vuoden aikana, kun vastaanottimet alkoivat pienentyä ja halpenivat, on lisääntynyt määrä yrityksiä, jotka toimittavat GPS-laitteita satoihin erilaisiin sovelluksiin. Kaikille moderneille tekniikoille on myös tyypillistä, että tutkimukseen ja siihen liittyvään vastaanottimien kehittämiseen on käytetty valtavasti rahaa, mikä on johtanut huomattavaan parantumiseen vastaanottimen suorituskyvyssä. GPS-vastaanottimien kehitystyön lisäksi uusien maailmanlaajuisten satelliittinavigointijärjestelmien, kuten venäläisen GLONASS, kiinalaisen BeiDou- ja eurooppalaisen Galileo-järjestelmien käyttöönotto tarjoaa entistä enemmän mahdollisuuksia suorituskyvyn parantamiseen. Sekä GPS että nämä uudet järjestelmät ovat myös ottaneet käyttöön uudentyyppisiä signaalirakenteita, jotka voivat tarjota parempilaatuisia havaintoja ja siten parantaa kaikkien vastaanottimien suorituskykyä. Lopuksi menetelmät, kuten PPP ja RTK, jotka aiemmin olivat varattu ammattikäyttäjille, ovat tulleet kuluttajamarkkinoille mahdollistaen ennennäkemättömän suorituskyvyn jokaiselle satelliittinavigointivastaanottimien käyttäjälle. Tässä opinnäytetyössä arvioidaan tämän kehityksen vaikutusta sekä suorituskykyyn että vastaanottimen arkkitehtuuriin. Työssä esitellään yksityiskohtaisesti FGI:ssä kehitetyn ohjelmistopohjaisen vastaanottimen, FGI-GSRx:n. Tämän vastaanottimen avulla on työssä arvioitu miten sekä uudet konstellaatiot että uudet nykyaikaiset signaalit ja niitten seurantamenetelmät vaikuttavat suorituskykyyn ja vastaanotin arkkitehtuuriin. Tämän lisäksi on arvioitu PPP- ja RTK-tarkkuuspaikannusmenetelmien vaikutus FinnRefCORS-verkkoa käyttäen useiden erityyppisten vastaanottimien kanssa, mukaan lukien kuluttajalaatuiset vastaanottimet. Tulokset osoittavat, että enemmän konstellaatioita ja signaaleja käytettäessä paikannusratkaisun tarkkuus paranee 3 metristä 1,4 metriin hyvissä olosuhteissa ja yli 10-kertaiseksi tiheästi rakennetuissa kaupungeissa, jossa käytettävissä olevien signaalien määrä kasvaa kertoimella 2 käytettäessä kolmea konstellaatiota. Uusia moderneja modulaatiotekniikoita, kuten BOC-modulaatiota, käytettäessä tulokset osoittavat Galileo-ratkaisun tarkkuuden paranevan lähes 25%:lla ja esitelty uusi signaalinkäsittelymenetelmä lisää tällaisen tarkkuuden saatavuutta 50%:sta lähes 100%:iin. Lopuksi tarkkuuspaikannusmenetelmien tulokset osoittavat, että 15 cm:n tarkkuus on saavutettavissa, mikä on merkittävä parannus verrattuna 1,4 metrin tarkkuuteen. Näiden parannusten saavuttamiseksi on olennaista, että itse vastaanotin on mukautettu hyödyntämään näitä uusia signaaleja ja konstellaatioita. Tämä tarkoittaa, että nykyaikaisten kuluttajamarkkinoiden vastaanottimien suunnittelu on haastavaa ja monissa tapauksissa ohjelmistopohjainen vastaanotin olisi parempi ja halvempi valinta kuin uusien mikropiirien kehittäminen.For the last three decades, satellite navigation has evolved from being a technology for professional and military users to a technology available for everyone. Especially during the last 15 years, since the receivers started getting smaller and cheaper, there has been an increasing number of companies delivering Global Positioning System (GPS) enabled devices for hundreds of different kind of applications. Typical for any modern technology, there has also been an enormous amount of money spent on research and accompanied receiver development resulting in an immense increase in receiver performance. In addition to the development efforts on GPS receivers the introduction of new global navigation satellite systems such as the Russian Globalnaja Navigatsionnaja Sputnikovaja Sistema (GLONASS), the Chinese BeiDou, and the European Galileo systems offers even more opportunities for improved performance. Both GPS and these new systems have also introduced new types of signal structures that can provide better quality observations and even further improve the performance of all receivers. Finally, methods like Precise Point Positioning (PPP) and Real Time Kinematic (RTK) that earlier were reserved for professional users have entered into the consumer market enabling never before seen performance for every user of satellite navigation receivers. This thesis will assess the impact of this development on both performance as well as on receiver architecture. The design of the software defined receiver developed at FGI, the FGI-GSRx, is presented in detail in this thesis. This receiver has then been used to assess the impact of using multiple constellations as well as new novel signal processing methods for modern signals. To evaluate the impact of PPP and RTK methods the FinnRef Continuously Operating Reference Station (CORS) network has been used together with several different types of receivers including consumer grade off the shelf receivers. The results show that when using more constellations and signals the accuracy of the positioning solution improves from3 meters to 1.4 meters in open sky conditions and by more than a factor 10 in severe urban canyons. For severe urban canyons the available also increases by a factor 2 when using three constellations. When using new modern modulation techniques like high order BOC results show an accuracy improvement for a Galileo solution of almost 25 % and the presented new signal processing method increase the availability of such an accuracy from 50 % to almost 100 %. Finally, results from precise point positioning methods show that an accuracy of 15 cm is achievable, which is a significant improvement compared to an accuracy of 1.4 m for a standalone multi constellation solution. To achieve these improvements, it is essential that the receiver itself is adapted to make use of these new signals and constellations. This means that the design of modern consumer market receivers is challenging and in many cases a software define receiver would be a better and cheaper choice than developing new Application Specific Integrated Circuit (ASIC)’s

Trajectory determination and analysis in sports by satellite and inertial navigation

This research presents methods for performance analysis in sports through the integration of Global Positioning System (GPS) measurements with Inertial Navigation System (INS). The described approach focuses on strapdown inertial navigation using Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU). A simple inertial error model is proposed and its relevance is proven by comparison to reference data. The concept is then extended to a setup employing several MEMS-IMUs in parallel. The performance of the system is validated with experiments in skiing and motorcycling. The position accuracy achieved with the integrated system varies from decimeter level with dual-frequency differential GPS (DGPS) to 0.7 m for low-cost, single-frequency DGPS. Unlike the position, the velocity accuracy (0.2 m/s) and orientation accuracy (1 – 2 deg) are almost insensitive to the choice of the receiver hardware. The orientation performance, however, is improved by 30 – 50% when integrating four MEMS-IMUs in skew-redundant configuration. Later part of this research introduces a methodology for trajectory comparison. It is shown that trajectories based on dual-frequency GPS positions can be directly modeled and compared using cubic spline smoothing, while those derived from single-frequency DGPS require additional filtering and matching

NB-IoT via non terrestrial networks

Massive Internet of Things is expected to play a crucial role in Beyond 5G (B5G) wireless communication systems, offering seamless connectivity among heterogeneous devices without human intervention. However, the exponential proliferation of smart devices and IoT networks, relying solely on terrestrial networks, may not fully meet the demanding IoT requirements in terms of bandwidth and connectivity, especially in areas where terrestrial infrastructures are not economically viable. To unleash the full potential of 5G and B5G networks and enable seamless connectivity everywhere, the 3GPP envisions the integration of Non-Terrestrial Networks (NTNs) into the terrestrial ones starting from Release 17. However, this integration process requires modifications to the 5G standard to ensure reliable communications despite typical satellite channel impairments. In this framework, this thesis aims at proposing techniques at the Physical and Medium Access Control layers that require minimal adaptations in the current NB-IoT standard via NTN. Thus, firstly the satellite impairments are evaluated and, then, a detailed link budget analysis is provided. Following, analyses at the link and the system levels are conducted. In the former case, a novel algorithm leveraging time-frequency analysis is proposed to detect orthogonal preambles and estimate the signals’ arrival time. Besides, the effects of collisions on the detection probability and Bit Error Rate are investigated and Non-Orthogonal Multiple Access approaches are proposed in the random access and data phases. The system analysis evaluates the performance of random access in case of congestion. Various access parameters are tested in different satellite scenarios, and the performance is measured in terms of access probability and time required to complete the procedure. Finally, a heuristic algorithm is proposed to jointly design the access and data phases, determining the number of satellite passages, the Random Access Periodicity, and the number of uplink repetitions that maximize the system's spectral efficiency

Data Fusion Architecture with Integrity Monitoring for State Estimation in Automated Driving

In the advent of automated driving, numerous system architectures to reach this goal are currently being developed. In this process, safety and modularity criteria are becoming increasingly important. This opens up new opportunities in the system design, but also creates new challenges. All functions or services in an automated vehicle are affected by this, including the estimation of the vehicle's dynamic state. Requirements for the Vehicle Dynamic State Estimation demand integrity measures and high system reliability, which can often only be achieved by redundant structures. A federated data fusion architecture with integrity monitoring is presented in this work to fulfill the aforementioned requirements for the Vehicle Dynamic State Estimation in automated driving. It inputs several redundant multi-sensor data fusion filters in a first fusion layer whose results are combined in a second fusion layer. This second layer implements plausibility checks and combines voting and data fusion, both utilizing the integrity measures of the redundant data fusion filters in the first layer. To compute such integrity measures, three integrity monitoring concepts are developed which differ substantially in their error modeling and complexity. The first concept represents the traditional approach and relies on the fusion filter's estimated covariances assuming that the error is normally distributed. The second concept models the errors in the fusion filter's output as the sum of errors from the filter's inputs. The error of each input is modeled as a multi-variate Student distribution and propagated through the filter. Utilizing the resulting error distribution, the integrity measures are computed. The third concept is based on the principle of Multiple Hypothesis Solution Separation. Subsets consisting of parts of the available filter inputs are formed and the fusion for each of these subsets is computed. Integrity measures are deducted by comparing the subset's results with each other. Each of the three concepts outputs a protection level, which is compared to an alert limit for integrity monitoring. Additionally, one of the redundant multi-sensor data fusion filters used in the federated fusion architecture is presented in this work. An Error-State Extended Kalman Filter is implemented to fuse the observations of three sensor types: a dual-antenna Global Navigation Satellite System Receiver, an Inertial Measurement Unit and wheel odometry sensors measuring wheel speeds and steering angles. The implementation includes sensor error models providing suitable error covariances for the filter's measurement updates and Fault Detection & Exclusion methods to increase the filter's robustness. Besides the measurement updates from the mentioned sensor types, also zero updates, i.e., zero velocity and zero angular rate updates, are part of the implementation, which are executed when a standstill of the vehicle is detected. In order to evaluate the performance of the presented fusion filter, integrity algorithms and fusion architecture, an extensive set of measurements with a total duration of more than 23 hours is used. The measurements are divided into four categories according to their environmental and satellite reception conditions, since these have a strong influence on the filter's performance. All in all, the integrity requirements are met by all implemented algorithms in favorable satellite reception conditions. However, only the second concept using multi-variate Student distributions for error modeling does so in all measurement categories, from ideal satellite reception conditions in the test track category to very challenging environments in the urban category. The federated data fusion architecture also fulfills the integrity requirements with only one minor exception, maintaining or even reducing the empirical integrity risk depending on the measurement category. Additionally, the accuracy and availability is improved significantly in most cases, compared to the fusion filters in its first layer. Finally, the usability of the developed concepts is demonstrated by applying them in a prototype vehicle of the research project UNICARagil. The integration in the system architecture and the interaction with other services is presented. In measurements from the commissioning tests the functionality is illustrated

A New Asynchronous RTK Method to Mitigate Base Station Observation Outages

Real-time kinematic (RTK) positioning is a satellite navigation technique that is widely used to enhance the precision of position data obtained from global navigation satellite systems (GNSS). This technique can reduce or eliminate significant correlation errors via the enhancement of the base station observation data. However, observations received by the base station are often interrupted, delayed, and/or discontinuous, and in the absence of base station observation data the corresponding positioning accuracy of a rover declines rapidly. With the strategies proposed till date, the positioning accuracy can only be maintained at the centimeter-level for a short span of time, no more than three min. To address this, a novel asynchronous RTK method (that addresses asynchronous errors) that can bridge significant gaps in the observations at the base station is proposed. First, satellite clock and orbital errors are eliminated using the products of the final precise ephemeris during post-processing or the ultra-rapid precise ephemeris during real-time processing. Then the tropospheric error is corrected using the Saastamoinen model and the asynchronous ionospheric delay is corrected using the carrier phase measurements from the rover receiver. Finally, a straightforward first-degree polynomial function is used to predict the residual asynchronous error. Experimental results demonstrate that the proposed approach can achieve centimeter-level accuracy for as long as 15 min during interruptions in both real-time and post-processing scenarios, and that the accuracy of the real-time scheme can be maintained for 15 min even when a large systematic error is projected in the U direction