IMM-Based lane-change prediction in highways with low-cost GPS/INS

The prediction of lane changes has been proven to be useful for collision avoidance support in road vehicles. This paper proposes an interactive multiple model (IMM)-based method for predicting lane changes in highways. The sensor unit consists of a set of low-cost Global Positioning System/inertial measurement unit (GPS/IMU) sensors and an odometry captor for collecting velocity measurements. Extended Kalman filters (EKFs) running in parallel and integrated by an IMM-based algorithm provide positioning and maneuver predictions to the user. The maneuver states Change Lane (CL) and Keep Lane (KL) are defined by two models that describe different dynamics. Different model sets have been studied to meet the needs of the IMM-based algorithm. Real trials in highway scenarios show the capability of the system to predict lane changes in straight and curved road stretches with very short latency times.Ministerio de Fomento: FOM/2454/200

On the vehicle sideslip angle estimation: a literature review of methods, models and innovations

Typical active safety systems controlling the dynamics of passenger cars rely on real-time monitoring of the vehicle sideslip angle (VSA), together with other signals like wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, known as the yaw rate. The VSA (aka attitude or “drifting” angle) is defined as the angle between the vehicle longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory therefore it is a vital piece of information enabling directional stability assessment, in transients following emergency manoeuvres for instance. As explained in the introduction the VSA is not measured directly for impracticality and it is estimated on the basis of available measurements like wheel velocities, linear and angular accelerations etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e. Observer-based and Neural Network-based, focusing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. Advantages and limitations of each technique have been highlighted and discussed

Kernel-based fault diagnosis of inertial sensors using analytical redundancy

Kernel methods are able to exploit high-dimensional spaces for representational advantage, while only operating implicitly in such spaces, thus incurring none of the computational cost of doing so. They appear to have the potential to advance the state of the art in control and signal processing applications and are increasingly seeing adoption across these domains. Applications of kernel methods to fault detection and isolation (FDI) have been reported, but few in aerospace research, though they offer a promising way to perform or enhance fault detection. It is mostly in process monitoring, in the chemical processing industry for example, that these techniques have found broader application. This research work explores the use of kernel-based solutions in model-based fault diagnosis for aerospace systems. Specifically, it investigates the application of these techniques to the detection and isolation of IMU/INS sensor faults – a canonical open problem in the aerospace field. Kernel PCA, a kernelised non-linear extension of the well-known principal component analysis (PCA) algorithm, is implemented to tackle IMU fault monitoring. An isolation scheme is extrapolated based on the strong duality known to exist between probably the most widely practiced method of FDI in the aerospace domain – the parity space technique – and linear principal component analysis. The algorithm, termed partial kernel PCA, benefits from the isolation properties of the parity space method as well as the non-linear approximation ability of kernel PCA. Further, a number of unscented non-linear filters for FDI are implemented, equipped with data-driven transition models based on Gaussian processes - a non-parametric Bayesian kernel method. A distributed estimation architecture is proposed, which besides fault diagnosis can contemporaneously perform sensor fusion. It also allows for decoupling faulty sensors from the navigation solution

A distributed architecture for unmanned aerial systems based on publish/subscribe messaging and simultaneous localisation and mapping (SLAM) testbed

A dissertation submitted in fulfilment for the degree of Master of Science. School of Computational and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa, November 2017The increased capabilities and lower cost of Micro Aerial Vehicles (MAVs) unveil big opportunities for a rapidly growing number of civilian and commercial applications. Some missions require direct control using a receiver in a point-to-point connection, involving one or very few MAVs. An alternative class of mission is remotely controlled, with the control of the drone automated to a certain extent using mission planning software and autopilot systems. For most emerging missions, there is a need for more autonomous, cooperative control of MAVs, as well as more complex data processing from sensors like cameras and laser scanners. In the last decade, this has given rise to an extensive research from both academia and industry. This research direction applies robotics and computer vision concepts to Unmanned Aerial Systems (UASs). However, UASs are often designed for specific hardware and software, thus providing limited integration, interoperability and re-usability across different missions. In addition, there are numerous open issues related to UAS command, control and communication(C3), and multi-MAVs. We argue and elaborate throughout this dissertation that some of the recent standardbased publish/subscribe communication protocols can solve many of these challenges and meet the non-functional requirements of MAV robotics applications. This dissertation assesses the MQTT, DDS and TCPROS protocols in a distributed architecture of a UAS control system and Ground Control Station software. While TCPROS has been the leading robotics communication transport for ROS applications, MQTT and DDS are lightweight enough to be used for data exchange between distributed systems of aerial robots. Furthermore, MQTT and DDS are based on industry standards to foster communication interoperability of “things”. Both protocols have been extensively presented to address many of today’s needs related to networks based on the internet of things (IoT). For example, MQTT has been used to exchange data with space probes, whereas DDS was employed for aerospace defence and applications of smart cities. We designed and implemented a distributed UAS architecture based on each publish/subscribe protocol TCPROS, MQTT and DDS. The proposed communication systems were tested with a vision-based Simultaneous Localisation and Mapping (SLAM) system involving three Parrot AR Drone2 MAVs. Within the context of this study, MQTT and DDS messaging frameworks serve the purpose of abstracting UAS complexity and heterogeneity. Additionally, these protocols are expected to provide low-latency communication and scale up to meet the requirements of real-time remote sensing applications. The most important contribution of this work is the implementation of a complete distributed communication architecture for multi-MAVs. Furthermore, we assess the viability of this architecture and benchmark the performance of the protocols in relation to an autonomous quadcopter navigation testbed composed of a SLAM algorithm, an extended Kalman filter and a PID controller.XL201

Overcoming the challenges of low-cost inertial navigation

Inertial navigation is always available as a base for multisensor navigation systems on, because it requires no external signals. However, measurement errors persist and grow with time so accurate calibration is crucial. Large systematic errors are present in the micro-electro-mechanical sensors (MEMS) whose low cost brings inertial navigation to many new applications. Using factory-calibrated MEMS another navigation technology can calibrate these errors with in-run estimation using a Kalman filter (KF). However, the raw systematic errors of low-cost MEMS are often too large for stable performance. This thesis contributes to knowledge in three areas. First, it takes a simple GNSS-inertial KF and examines the levels of the various systematic errors which cause the integration to fail. This allows the user to know how well calibrated the sensors need to be to use in-run calibration. Second, the thesis examines how the end-user could conduct a calibration: it analyses one method in detail showing how imperfections in the procedure affect the results and comparing calculation methods. This is important as frequently calibration methods are only validated by demonstrating consistent results for one particular sensor. These two are primarily accomplished using statistical Monte Carlo simulations. Third, techniques are examined by which an array of inertial sensors could be used to produce an output which is better than the simple array average. This includes methods that reduce the array’s sensitivity to environmental conditions, this is important because the sensors’ calibration typically depends strongly on temperature. Also included in the thesis are descriptions of experimental hardware and experiments which have been carried to support and unify the other parts of the thesis. Overall, this thesis’ contributions will help make low-cost inertial navigation systems more accurate and will allow system designers to concentrate effort where it will make the most difference

Seamless Positioning and Navigation in Urban Environment

L'abstract è presente nell'allegato / the abstract is in the attachmen

Autonomous terminal area operations for unmanned aerial systems

After many years of successful operation in military domains, Unmanned Aerial Systems (UASs) are generating significant interest amongst civilian operators in sectors such as law enforcement, search and rescue, aerial photography and mapping. To maximise the benefits brought by UASs to sectors such as these, a high level of autonomy is desirable to reduce the need for highly skilled operators. Highly autonomous UASs require a high level of situation awareness in order to make appropriate decisions. This is of particular importance to civilian UASs where transparency and equivalence of operation to current manned aircraft is a requirement, particularly in the terminal area immediately surrounding an airfield. This thesis presents an artificial situation awareness system for an autonomous UAS capable of comprehending both the current continuous and discrete states of traffic vehicles. This estimate forms the basis of the projection element of situation awareness, predicting the future states of traffic. Projection is subject to a large degree of uncertainty in both continuous state variables and in the execution of intent information by the pilot. Both of these sources of uncertainty are captured to fully quantify the future positions of traffic. Based upon the projection of future traffic positions a self separation system is designed which allows an UAS to quantify its separation to traffic vehicles up to some future time and manoeuvre appropriately to minimise the potential for conflict. A high fidelity simulation environment has been developed to test the performance of the artificial situation awareness and self separation system. The system has demonstrated good performance under all situations, with an equivalent level of safety to that of a human pilot

Indoor collaborative positioning based on a multi-sensor and multi-user system

With recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location-based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing datWith recent developments in the Global Satellite Navigation Systems (GNSS), the applications and services of positioning and navigation have developed rapidly worldwide. Location based services (LBS) have become a big application which provide position related services to the mass market. As LBS applications become more popular, positioning services and capacity are demanded to cover all types of environment with improved accuracy and reliability. While GNSS can provide promising positioning and navigation solutions in open outdoor environments, it does not work well when inside buildings, in tunnels or under canopy. Positioning in such difficult environments have been known as the indoor positioning problem. Although the problem has been looked into for more than a decade, there currently no solution that can compare to the performance of GNSS in outdoor environments. This thesis introduces a collaborative indoor positioning solution based on particle filtering which integrates multiple sensors, e.g. inertial sensors, Wi-Fi signals, map information etc., and multiple local users which provide peer-to-peer (P2P) relative ranging measurements. This solution addresses three current problems of indoor positioning. First of all is the positioning accuracy, which is limited by the availability of sensors and the quality of their signals in the environment. The collaborative positioning solution integrates a number of sensors and users to provide better measurements and restrict measurement error from growing. Secondly, the reliability of the positioning solutions, which is also affected by the signal quality. The unpredictable behaviour of positioning signals and data could lead to many uncertainties in the final positioning result. A successful positioning system should be able to deal with changes in the signal and provide reliable positioning results using different data processing strategies. Thirdly, the continuity and robustness of positioning solutions. While the indoor environment can be very different from one another, hence applicable signals are also different, the positioning solution should take into account the uniqueness of different situations and provide continuous positioning result regardless of the changing data. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable. The collaborative positioning aspect is examined from three aspects, the network geometry, the network size and the P2P ranging measurement accuracy. Both theoretical and experimental results indicate that a collaborative network with a low dilution of precision (DOP) value could achieve better positioning accuracy. While increasing sensors and users will reduce DOP, it will also increase computation load which is already a disadvantage of particle filters. The most effective collaborative positioning network size is thus identified and applied. While the positioning system measurement error is constrained by the accuracy of the P2P ranging constraint, the work in this thesis shows that even low accuracy measurements can provide effective constraint as long as the system is able to identify the different qualities of the measurements. The proposed collaborative positioning algorithm constrains both inertial measurements and Wi-Fi fingerprinting to enhance the stability and accuracy of positioning result, achieving metre-level accuracy. The application of collaborative constraints also eliminate the requirement for indoor map matching which had been a very useful tool in particle filters for indoor positioning purposes. The wall constraint can be replaced flexibly and easily with relative constraint. Simulations and indoor trials are carried out to evaluate the algorithms. Results indicate that metre-level positioning accuracy could be achieved and collaborative positioning also gives the system more flexibility to adapt to different situations when Wi-Fi or collaborative ranging is unavailable

Sensors, measurement fusion and missile trajectory optimisation

When considering advances in “smart” weapons it is clear that air-launched systems have adopted an integrated approach to meet rigorous requirements, whereas air-defence systems have not. The demands on sensors, state observation, missile guidance, and simulation for air-defence is the subject of this research. Historical reviews for each topic, justification of favoured techniques and algorithms are provided, using a nomenclature developed to unify these disciplines. Sensors selected for their enduring impact on future systems are described and simulation models provided. Complex internal systems are reduced to simpler models capable of replicating dominant features, particularly those that adversely effect state observers. Of the state observer architectures considered, a distributed system comprising ground based target and own-missile tracking, data up-link, and on-board missile measurement and track fusion is the natural choice for air-defence. An IMM is used to process radar measurements, combining the estimates from filters with different target dynamics. The remote missile state observer combines up-linked target tracks and missile plots with IMU and seeker data to provide optimal guidance information. The performance of traditional PN and CLOS missile guidance is the basis against which on-line trajectory optimisation is judged. Enhanced guidance laws are presented that demand more from the state observers, stressing the importance of time-to-go and transport delays in strap-down systems employing staring array technology. Algorithms for solving the guidance twopoint boundary value problems created from the missile state observer output using gradient projection in function space are presented. A simulation integrating these aspects was developed whose infrastructure, capable of supporting any dynamical model, is described in the air-defence context. MBDA have extended this work creating the Aircraft and Missile Integration Simulation (AMIS) for integrating different launchers and missiles. The maturity of the AMIS makes it a tool for developing pre-launch algorithms for modern air-launched missiles from modern military aircraft.EThOS - Electronic Theses Online ServiceGBUnited Kingdo