Today, a rapid development towards fuel efficient technological aids for vehicles is in progress. One step towards this is the development of platooning systems. The main concept of platooning is to let several heavy duty vehicles (HDVs) drive in a convoy and share important information with each other via wireless communication. This thesis describes one out of three subsystems in a project developed to handle the process from raw sensor data to control signal. The goal of the project is to achieve a safe and smooth control with the main purpose of reduced fuel consumption. This subsystem processes the raw sensor data received from the different HDVs. The purpose is to estimate the positions and velocities of the vehicles in a platoon, taking into account that packet-loss, out of sequence measurements and irrelevant information can occur. This is achieved by filtering the information from different sensors in an Extended Kalman Filter and converting it into a local coordinate system with the origin in the ego vehicle. Moreover, the estimates are sorted and categorized into classes with respect to the status of the vehicles. The result of the thesis is useful estimates that are independent of outer effects in a local reference system with origin in the host vehicle. This information can then be used for further sensor fusion and implementation of a Model Predictive Controller (MPC) in two other subsystems. These three subsystems result in a smooth and safe control with an average reduced fuel consumption of approxi- mately 11.1% when the vehicles drive with a distance of 0.5 seconds in a simulated environment. / Dagens utveckling inom fordonsindustrin fokuserar mer och mer påutveckling av bränsleeffektiva hjälpmedel. Ett steg i denna riktning är utvecklingen av platooningsystem. Huvudkonceptet med platooning är att låta flera tunga fordon köra i följd i en konvoj och dela viktig information med varandra via trådlös kommuni- kation och en automatiserad styrstrategi. Detta examensarbete beskriver ett utav tre delsystem i ett projekt som är utvecklat för att hantera en process från rå sensordata till styrsignaler för fordonen. Målet är att uppnå en säker och mjuk reglering med huvudsyftet att reducera bränsleförbrukningen. Det här delsystemet behandlar mottagen sensordata från de olika fordonen. Målet med delsystemet är att skatta positioner och hastigheter för fordonen i konvojen med hänsyn till att förlorad, försenad eller irrelevant information från det trådlösa nätverket kan förekomma. Detta uppnås genom filtrering i ett Extended Kalman Filter och konvertering till ett lokalt referenssystem med origo i det egna fordo- net. Utöver detta sorteras informationen och kategoriseras in i olika klasser efter fordonens status. Examensarbetet resulterade i användbara skattningar oberoende av yttre om- ständigheter i ett lokalt referenssystem med origo i det egna fordonet. Denna information kan användas vidare för ytterligare sensorfusion och implementering av en modellbaserad prediktionsregulator (MPC) i två andra delsystem. De tre delsystemen resulterade i en mjuk och säker reglering och en reducerad bränsleför- brukning med i genomsnitt 11.1% då fordonen körde med 0.5 sekunders avstånd i en simulerad miljö.

A model based algorithm of pipeline flow is developed and tested to determine if the model is capable of detecting a leak in a pipeline. The overall objective of this research is to determine the feasibility of applying the Extended Kalman Filter and a new technique defined as the Extended Boundary Approach to the detection of leakages in a physical water distribution system. <p>The demands on the water supply system increase as the human population grows and expands throughout the world. Water conservation is required to ensure an adequate supply of water remains for future generations. One way to conserve this water is by reducing the leakages in underground water distribution systems. Currently between 10 to 50 percent of the pumped water is lost due to unrecognized leakages. This results in a huge revenue loss of water, chemicals and energy that is required for transporting the water. The detection of underground leakages is a very complex problem because many leakages are small and go unnoticed by todays leak detection technology. <p>A model based leak detection technique is developed and tested in this thesis. The Method of Characteristics is used to develop a model of a single pipeline. This method is extensively used and provides the most accurate results of the two partial differential equations of continuity and momentum that describe pipe flow. The Extended Kalman Filter is used to estimate two fictitious leakages at known locations along the pipeline. In order to ensure the model is observable four pressure measurements are needed at equally spaced nodes along the pipeline. With the development of the Extended Boundary Approach only the upstream and downstream pressure measurements are required, however; the upstream and downstream flow measurements are also required. Using the information from the two fictitious leaks the actual leak location and magnitude can be determined. This method is only capable of detecting one leak in a single pipeline. <p>The results of the developed model show that the approach is capable of theoretically determining the leak location and magnitude in a pipeline. However, at this time, the feasibility of implementing the proposed leak detection method is limited by the required level of accuracy of the sensors which is beyond that found in todays technology. It was also found that the EKF used primarily steady state information to predict the leakage. It is recommended that further research explore alternate models which might better enhance the EKF approach using transient information from the pipeline. This may allow implementation on a real pipeline.

The aim of platooning is to enable several Heavy Duty Vehicles (HDVs) to drive in a convoy and act as one unit to decrease the fuel consumption. By introducing wireless communication and tight control, the distance between the HDVs can be decreased significantly. This implies a reduction of the air drag and consequently the fuel consumption for all the HDVs in the platoon. The challenge in platooning is to keep the HDVs as close as possible to each other without endangering safety. Therefore, sensor fusion is necessary to get an accurate estimate of the relative distance and velocity, which is a pre-requisite for the controller. This master thesis aims at developing a sensor fusion framework from on-board sensor information as well as other vehicles’ sensor information communicated over a WiFi link. The most important sensors are GPS, that gives a rough position of each HDV, and radar that provides relative distance for each pair of HDV’s in the platoon. A distributed solution is developed, where an Extended Kalman Filter (EKF) estimates the state of the whole platoon. The state vector includes position, velocity and length of each HDV, which is used in a Model Predictive Control (MPC). Furthermore, a method is discussed on how to handle vehicles outside the platoon and how various road surfaces can be managed. This master thesis is a part of a project consisting of three parallel master’s theses. The other two master’s theses investigate and implement rough pre-processing of data, time synchronization and MPC associated with platooning. It was found that the three implemented systems could reduce the average fuel consumption by 11.1 %.

A model based algorithm of pipeline flow is developed and tested to determine if the model is capable of detecting a leak in a pipeline. The overall objective of this research is to determine the feasibility of applying the Extended Kalman Filter and a new technique defined as the Extended Boundary Approach to the detection of leakages in a physical water distribution system. <p>The demands on the water supply system increase as the human population grows and expands throughout the world. Water conservation is required to ensure an adequate supply of water remains for future generations. One way to conserve this water is by reducing the leakages in underground water distribution systems. Currently between 10 to 50 percent of the pumped water is lost due to unrecognized leakages. This results in a huge revenue loss of water, chemicals and energy that is required for transporting the water. The detection of underground leakages is a very complex problem because many leakages are small and go unnoticed by todays leak detection technology. <p>A model based leak detection technique is developed and tested in this thesis. The Method of Characteristics is used to develop a model of a single pipeline. This method is extensively used and provides the most accurate results of the two partial differential equations of continuity and momentum that describe pipe flow. The Extended Kalman Filter is used to estimate two fictitious leakages at known locations along the pipeline. In order to ensure the model is observable four pressure measurements are needed at equally spaced nodes along the pipeline. With the development of the Extended Boundary Approach only the upstream and downstream pressure measurements are required, however; the upstream and downstream flow measurements are also required. Using the information from the two fictitious leaks the actual leak location and magnitude can be determined. This method is only capable of detecting one leak in a single pipeline. <p>The results of the developed model show that the approach is capable of theoretically determining the leak location and magnitude in a pipeline. However, at this time, the feasibility of implementing the proposed leak detection method is limited by the required level of accuracy of the sensors which is beyond that found in todays technology. It was also found that the EKF used primarily steady state information to predict the leakage. It is recommended that further research explore alternate models which might better enhance the EKF approach using transient information from the pipeline. This may allow implementation on a real pipeline.

The Defence, Peace, Safety and Security (DPSS) competency area within the Council for Scientific and Industrial Research (CSIR) has identified the need for the development of a robot that can operate in almost any land-based environment. Legged robots, especially hexapod (six-legged) robots present a wide variety of advantages that can be utilised in this environment and is identified as a feasible solution. The biggest advantage and main reason for the development of legged robots over mobile (wheeled) robots, is their ability to navigate in uneven, unstructured terrain. However, due to the complicated control algorithms needed by a legged robot, most literature only focus on navigation in even or relatively even terrains. This is seen as the main limitation with regards to the development of legged robot applications. For navigation in unstructured terrain, postural controllers of legged robots need fast and precise knowledge about the state of the robot they are regulating. The speed and accuracy of the state estimation of a legged robot is therefore very important. Even though state estimation for mobile robots has been studied thoroughly, limited research is available on state estimation with regards to legged robots. Compared to mobile robots, locomotion of legged robots make use of intermitted ground contacts. Therefore, stability is a main concern when navigating in unstructured terrain. In order to control the stability of a legged robot, six degrees of freedom information is needed about the base of the robot platform. This information needs to be estimated using measurements from the robot’s sensory system. A sensory system of a robot usually consist of multiple sensory devices on board of the robot. However, legged robots have limited payload capacities and therefore the amount of sensory devices on a legged robot platform should be kept to a minimum. Furthermore, exteroceptive sensory devices commonly used in state estimation, such as a GPS or cameras, are not suitable when navigating in unstructured and unknown terrain. The control and localisation of a legged robot should therefore only depend on proprioceptive sensors. The need for the development of a reliable state estimation framework (that only relies on proprioceptive information) for a low-cost, commonly available hexapod robot is identified. This will accelerate the process for control algorithm development. In this study this need is addressed. Common proprioceptive sensors are integrated on a commercial low-cost hexapod robot to develop the robot platform used in this study. A state estimation framework for legged robots is used to develop a state estimation methodology for the hexapod platform. A kinematic model is also derived and verified for the platform, and measurement models are derived to address possible errors and noise in sensor measurements. The state estimation methodology makes use of an Extended Kalman filter to fuse the robots kinematics with measurements from an IMU. The needed state estimation equations are also derived and implemented in Matlab®. The state estimation methodology developed is then tested with multiple experiments using the robot platform. In these experiments the robot platform captures the sensory data with a data acquisition method developed while it is being tracked with a Vicon motion capturing system. The sensor data is then used as an input to the state estimation equations in Matlab® and the results are compared to the ground-truth measurement outputs of the Vicon system. The results of these experiments show very accurate estimation of the robot and therefore validate the state estimation methodology and this study. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

The Defence, Peace, Safety and Security (DPSS) competency area within the Council for Scientific and Industrial Research (CSIR) has identified the need for the development of a robot that can operate in almost any land-based environment. Legged robots, especially hexapod (six-legged) robots present a wide variety of advantages that can be utilised in this environment and is identified as a feasible solution. The biggest advantage and main reason for the development of legged robots over mobile (wheeled) robots, is their ability to navigate in uneven, unstructured terrain. However, due to the complicated control algorithms needed by a legged robot, most literature only focus on navigation in even or relatively even terrains. This is seen as the main limitation with regards to the development of legged robot applications. For navigation in unstructured terrain, postural controllers of legged robots need fast and precise knowledge about the state of the robot they are regulating. The speed and accuracy of the state estimation of a legged robot is therefore very important. Even though state estimation for mobile robots has been studied thoroughly, limited research is available on state estimation with regards to legged robots. Compared to mobile robots, locomotion of legged robots make use of intermitted ground contacts. Therefore, stability is a main concern when navigating in unstructured terrain. In order to control the stability of a legged robot, six degrees of freedom information is needed about the base of the robot platform. This information needs to be estimated using measurements from the robot’s sensory system. A sensory system of a robot usually consist of multiple sensory devices on board of the robot. However, legged robots have limited payload capacities and therefore the amount of sensory devices on a legged robot platform should be kept to a minimum. Furthermore, exteroceptive sensory devices commonly used in state estimation, such as a GPS or cameras, are not suitable when navigating in unstructured and unknown terrain. The control and localisation of a legged robot should therefore only depend on proprioceptive sensors. The need for the development of a reliable state estimation framework (that only relies on proprioceptive information) for a low-cost, commonly available hexapod robot is identified. This will accelerate the process for control algorithm development. In this study this need is addressed. Common proprioceptive sensors are integrated on a commercial low-cost hexapod robot to develop the robot platform used in this study. A state estimation framework for legged robots is used to develop a state estimation methodology for the hexapod platform. A kinematic model is also derived and verified for the platform, and measurement models are derived to address possible errors and noise in sensor measurements. The state estimation methodology makes use of an Extended Kalman filter to fuse the robots kinematics with measurements from an IMU. The needed state estimation equations are also derived and implemented in Matlab®. The state estimation methodology developed is then tested with multiple experiments using the robot platform. In these experiments the robot platform captures the sensory data with a data acquisition method developed while it is being tracked with a Vicon motion capturing system. The sensor data is then used as an input to the state estimation equations in Matlab® and the results are compared to the ground-truth measurement outputs of the Vicon system. The results of these experiments show very accurate estimation of the robot and therefore validate the state estimation methodology and this study. / MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / Pseudolites have been proposed for augmentation/replacement of the GPS system in radiolocation applications. However, a terrestrial pseudolite system suffers from the near-far effect due to received power disparities. Conventional code tracking loops as employed in GPS receivers are unable to suppress near-far interference. Here, a multiuser code tracking algorithm is presented based on the extended Kalman filter (EKF.) The EKF jointly tracks the delays and amplitudes of multiple received pseudolite waveforms. A modified EKF based on an approximate Bayesian estimator (BEKF) is also developed, which can in principle both acquire and track code delays, as well as detect loss-of-lock. Representative simulation results for the BEKF are presented for code tracking with 2 and 5 users.

Small satellites are becoming increasingly appealing as technology advances and shrinks in both size and cost. The development time for a small satellite is also much less compared to a large satellite. For small satellites to be successful, the navigation systems must be accurate and very often they must be autonomous. For lunar navigation, contact with a ground station is not always available and the system needs to be robust. The extended Kalman filter is a nonlinear estimator that has been used on-board spacecraft for decades. The filter requires linear approximations of the state and measurement models. In the past few years, the unscented Kalman filter has become popular and has been shown to reduce estimation errors. Additionally, the Jacobian matrices do not need to be derived in the unscented Kalman filter implementation. The intent of this research is to explore the capabilities of the extended Kalman filter and the unscented Kalman filter for use as a navigation algorithm on small satellites. The filters are applied to a satellite orbiting the Moon equipped with an inertial measurement unit, a sun sensor, a star camera, and a GPS-like sensor. The position, velocity, and attitude of the spacecraft are estimated along with sensor biases for the IMU accelerometer, IMU gyroscope, sun sensor and star camera. The estimation errors are compared for the extended Kalman filter and the unscented Kalman filter for the position, velocity and attitude. The analysis confirms that both navigation algorithms provided accurate position, velocity and attitude. The IMU gyroscope bias was observable for both filters while only the IMU accelerometer bias was observable with the extended Kalman filter. The sun sensor biases and the star camera biases were unobservable. In general, the unscented Kalman filter performed better than the extended Kalman filter in providing position, velocity, and attitude estimates but requires more computation time. / text

A Discrete Extended Kalman Filter has been designed to process measurements from a magnetometer, sun sensor, IMU, and GPS receiver to provide the relative position, velocity, attitude, and gyro bias of a chaser spacecraft relative to a target spacecraft. An Extended Kalman Filter with Uncompensated Bias has also been developed for the implementation of well known biases and errors that are not directly observable. A detailed explanation of the algorithms, models, and derivations that go into both filters is presented. With this simulation and specific sensor selection the position of the chaser spacecraft relative to the target can be estimated to within about 5 m, the velocity to within .1 m/s, and the attitude to within 2 degrees for both filters. If a thrust is applied to the IMU measurements, it takes about 1.5 minutes to get a good position estimate, using the Extended Kalman Filter with Uncompensated Bias. The error settles almost immediately using the general Extended Kalman Filter. These filters have been designed for and can be implemented on almost any small, low cost, low power satellite with this inexpensive set of sensors. / text

An important aspect for precision guidance, navigation, and control for lunar operations is environmental modeling. In particular, consider gravity field modeling. Available gravity field models for the Moon reach degree and order 165 requiring the use and storage of approximately 26,000 spherical harmonic coefficients. Although the high degree and order provide a means by which to accurately predict trajectories within the influence of the Moon's gravitational field, the size of these models makes using them computationally expensive and restricts their use in design environments with limited computer memory and storage. It is desirable to determine reduced complexity realizations of the gravitational models to lower the computational burden while retaining the structure of the original gravitational field for use in rapid design environments. The extended Kalman filter and the unscented Kalman filter are used to create reduced order models and are compared against a simple truncation based reduction method. Both variations of the Kalman filter out perform the truncation based method as a means by which to reduce the complexity of the gravitational field. The extended Kalman filter and unscented Kalman filter were able to achieve good estimates of position while reducing the number of spherical harmonic coefficients used in gravitational acceleration calculations by approximately 5,400, greatly increasing the speed of the calculations while reducing the required computer allocation. / text