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Prior results on target localization and circumnavigation with bearing measurements in $\mathbb {R}^{2}$ are extended with integral action, resulting in a control system that is robust to bounded load disturbances on the control inputs. Such disturbances may arise in practice due to modeling errors and need to be considered to ensure small tracking errors. The control inputs are modeled as the system velocities in the bearing and orthogonal bearing directions, facilitating an implementation on both aerial and ground vehicles. This is demonstrated in a simulation example, followed by an experimental application with ground-based vehicle circumnavigation, highlighting the performance and importance of using the proposed controllers.

This Master Thesis has two distinct parts. The first one mod- els an application of Graph Neural Networks (GNN) for the identifica- tion of critical nodes in graphs that correspond to traffic networks. We call critical nodes those that can compromise the traffic flow in some subgraphs of the network. Specifically, the example data for the demon- stration corresponds to the Vienna subway network, hence the linear subgraphs correspond to the subway lines with intersections at some key subway stations. Those critical nodes relative to a subway line compro- mise the traffic flow at this line, therefore, we propose three GNN based approaches for the identification of such critical nodes, reporting encour- aging results. The second part of the Master Thesis illustrates the back- ground research work on drone airspace management and a discussion of how the reported results may have some relevance for this emerging dif- ficult problem. The main idea is that the urban airspace for drones, that may be carrying out delivery of either persons (aerotaxis) or goods, can be structured along airways that mimic the existing network of streets. The computational example explored in part one of the Master Thesis, thus, becomes relevant for the development of intelligent drone airspace management.

The safety assessment process is a mandatory step in the development and certification of safety-critical systems such as the ones in the aerospace industry. In this work we show how Probabilistic Model Checking, a Formal Verification method, can help to assess the safety of navigation systems for a civil commercial transport category aircraft. The process involves a top-down approach identifying functions and its respective failure modes. Each failure event is associated with a hazard level, with an inverse relationship between the maximum acceptable probability and the event severity. Fault Tree Analysis (FTA) is the most commonly used method to quantify each events probability, but probabilistic models are also accepted as means of compliance demonstration, as per ARP-4761. Results show that the use of Probabilistic Model Checking as a means to complement non-formal methods is valuable, where we were able to evaluate the probability of several failure modes described in FAAs AC 20-138D, making use of Continuous-Time Markov Chains (CTMCs) with up 4.3 million of reachable states and 60 million of transitions., isbn: 978-303086972-4, Volume: 12952, Pages: 465-480

The safety assessment process is a mandatory step in the development and certification of safety-critical systems such as the ones in the aerospace industry. In this work we show how Probabilistic Model Checking, a Formal Verification method, can help to assess the safety of navigation systems for a civil commercial transport category aircraft. The process involves a top-down approach identifying functions and its respective failure modes. Each failure event is associated with a hazard level, with an inverse relationship between the maximum acceptable probability and the event severity. Fault Tree Analysis (FTA) is the most commonly used method to quantify each events probability, but probabilistic models are also accepted as means of compliance demonstration, as per ARP-4761. Results show that the use of Probabilistic Model Checking as a means to complement non-formal methods is valuable, where we were able to evaluate the probability of several failure modes described in FAAs AC 20-138D, making use of Continuous-Time Markov Chains (CTMCs) with up 4.3 million of reachable states and 60 million of transitions., Pages: 465-480

The safety assessment process is a mandatory step in the development and certification of safety-critical systems such as the ones in the aerospace industry. In this work we show how Probabilistic Model Checking, a Formal Verification method, can help to assess the safety of navigation systems for a civil commercial transport category aircraft. The process involves a top-down approach identifying functions and its respective failure modes. Each failure event is associated with a hazard level, with an inverse relationship between the maximum acceptable probability and the event severity. Fault Tree Analysis (FTA) is the most commonly used method to quantify each events probability, but probabilistic models are also accepted as means of compliance demonstration, as per ARP-4761. Results show that the use of Probabilistic Model Checking as a means to complement non-formal methods is valuable, where we were able to evaluate the probability of several failure modes described in FAAs AC 20-138D, making use of Continuous-Time Markov Chains (CTMCs) with up 4.3 million of reachable states and 60 million of transitions., Pages: 465-480

The safety assessment process is a mandatory step in the development and certification of safety-critical systems such as the ones in the aerospace industry. In this work we show how Probabilistic Model Checking, a Formal Verification method, can help to assess the safety of navigation systems for a civil commercial transport category aircraft. The process involves a top-down approach identifying functions and its respective failure modes. Each failure event is associated with a hazard level, with an inverse relationship between the maximum acceptable probability and the event severity. Fault Tree Analysis (FTA) is the most commonly used method to quantify each events probability, but probabilistic models are also accepted as means of compliance demonstration, as per ARP-4761. Results show that the use of Probabilistic Model Checking as a means to complement non-formal methods is valuable, where we were able to evaluate the probability of several failure modes described in FAAs AC 20-138D, making use of Continuous-Time Markov Chains (CTMCs) with up 4.3 million of reachable states and 60 million of transitions., isbn: 978-303086972-4, Volume: 12952, Pages: 465-480

The safety assessment process is a mandatory step in the development and certification of safety-critical systems such as the ones in the aerospace industry. In this work we show how Probabilistic Model Checking, a Formal Verification method, can help to assess the safety of navigation systems for a civil commercial transport category aircraft. The process involves a top-down approach identifying functions and its respective failure modes. Each failure event is associated with a hazard level, with an inverse relationship between the maximum acceptable probability and the event severity. Fault Tree Analysis (FTA) is the most commonly used method to quantify each events probability, but probabilistic models are also accepted as means of compliance demonstration, as per ARP-4761. Results show that the use of Probabilistic Model Checking as a means to complement non-formal methods is valuable, where we were able to evaluate the probability of several failure modes described in FAAs AC 20-138D, making use of Continuous-Time Markov Chains (CTMCs) with up 4.3 million of reachable states and 60 million of transitions., isbn: 978-303086972-4, Volume: 12952, Pages: 465-480

The safety assessment process is a mandatory step in the development and certification of safety-critical systems such as the ones in the aerospace industry. In this work we show how Probabilistic Model Checking, a Formal Verification method, can help to assess the safety of navigation systems for a civil commercial transport category aircraft. The process involves a top-down approach identifying functions and its respective failure modes. Each failure event is associated with a hazard level, with an inverse relationship between the maximum acceptable probability and the event severity. Fault Tree Analysis (FTA) is the most commonly used method to quantify each events probability, but probabilistic models are also accepted as means of compliance demonstration, as per ARP-4761. Results show that the use of Probabilistic Model Checking as a means to complement non-formal methods is valuable, where we were able to evaluate the probability of several failure modes described in FAAs AC 20-138D, making use of Continuous-Time Markov Chains (CTMCs) with up 4.3 million of reachable states and 60 million of transitions., isbn: 978-303086972-4, Volume: 12952, Pages: 465-480

The safety assessment process is a mandatory step in the development and certification of safety-critical systems such as the ones in the aerospace industry. In this work we show how Probabilistic Model Checking, a Formal Verification method, can help to assess the safety of navigation systems for a civil commercial transport category aircraft. The process involves a top-down approach identifying functions and its respective failure modes. Each failure event is associated with a hazard level, with an inverse relationship between the maximum acceptable probability and the event severity. Fault Tree Analysis (FTA) is the most commonly used method to quantify each events probability, but probabilistic models are also accepted as means of compliance demonstration, as per ARP-4761. Results show that the use of Probabilistic Model Checking as a means to complement non-formal methods is valuable, where we were able to evaluate the probability of several failure modes described in FAAs AC 20-138D, making use of Continuous-Time Markov Chains (CTMCs) with up 4.3 million of reachable states and 60 million of transitions., isbn: 978-303086972-4, Volume: 12952, Pages: 465-480

Trajectories optimisation is a major research topic in air transport and air traffic management, due to its profound impact both on passengers, airlines and the environment in general, and consequently on the perceived value and cost of air transportation. While the challenges associated to the optimisation of the en-route part of a flight are well understood, relative less attention has been devoted to the last part, i.e. the approach and landing. Here we show how open large-scale data sets of aircraft trajectories can be used to characterise the efficiency of flights landing at an airport, measured through the time and distance flown below 10,000 feet. The yielded picture is highly heterogeneous, with the time spent at low altitude varying from an average of 10 minutes for Zurich, up to 16 minutes for London Heathrow. Flights arriving at the same airport also experience highly different times, e.g. from 12 to 20 minutes for London Heathrow, depending on factors like traffic volumes, time of the year and of the day, and on interactions with other traffic patterns and airports. From a more general perspective, this contribution illustrates how the availability of large data sets can be used to improve our understanding of the real behaviour of the system, and especially its deviation from what planned.

Monitoring of potentially dangerous concentrations of gasses enclosed areas can be a mundane, demanding, and dangerous task and requires a large amount of, both manned hours as well as multiple sensors. Unmanned Aerial Vehicles can be modified to suit a range of different applications. This report gives an overview of the design, implementation, and testing process of a UAV that can perform autonomous flight, target detection, and air quality sensing in an indoor environment. The UAV is required to autonomously navigate through an indoor environment using waypoint navigation and A-Star pathfinding, and locate and confirm the locations of three separate visual targets using a custom coded computer vision solution that uses a camera on board. Once each target is located, the UAV hovers at each target location and performs air quality analysis for 10 seconds with two on board sensors. During testing, the UAV was able to autonomously build its own search pattern once it was made aware of any obstacles in the flight area. All three targets were autonomously confirmed and air quality analysis was successful. All of the important flight data was wirelessly transmitted to a ground control station which included a web page with a graphical user interface. The web page displayed a real time image stream, alongside dynamic positional data in an interactive map of the test area and air quality results in an easy to read format.

This paper reports the design, proof-of-concept implementation and preliminary performance assessment of a low-cost, software-defined spaceborne GNSS receiver. The presented approach takes advantage of the flexibility of software-defined radio technology and the forthcoming availability of radiation-hardened, space-certified Systems-on-Module to implement a fully customizable receiver with the capability to process GNSS signals in real-time and to deliver GNSS products in standard formats. The core GNSS engine is based on a free and open source software implementation of a multi-band, multi-system GNSS receiver released under the General Public License v3.0 and available in a public source code repository., Peer Reviewed, Postprint (published version)

Monitoring of environmental gases is a demanding task that can require long periods of observation and large numbers of sensors. Unmanned Aerial Vehicles (UAVs) presently represent a suitable alternative to monitor large, remote, and difficult to access areas. Due to the wide range and diversity of shape and size, UAVs now possess the capability of carrying specialized sensor modules that can accurately monitor gas concentrations. This paper describes the design and flight testing of a UAV which carries an on-board camera and a carbon dioxide gas sensor and is capable of autonomous gas sensing while simultaneously visually detecting predefined targets placed at locations inside a room. The detection system autonomously navigates around the flight area using a waypoint navigation system and hovers for 10 seconds once the target has been visually identified taking Air Quality (AQ) samples as it does so. Laboratory, bench and field test results demonstrate the capability of the UAV to detect targets placed in a room and analyse the air quality of samples taken during flight above the target. The data collected during the flight is transmitted, in real time, back to a Ground Control Station (GCS) for visualisation and analysis, where 3D mapping of the target location and gas concentration is presented via a web interface. Test cases verify the capability of the subsystem’s integration and the operation of the UAV system as a whole. The image processing algorithm used for the target detection, based on a cascade method, proved to be dependent on the frame transmission rates, which can make the software considerably slower. The developed system provides an effective monitoring system and can be used in a wide range of applications such as gas leaks, fires, and mining applications and if converted for use in an outdoor environment, applications such as agriculture biomass burning emissions and chemical and biological agent detection studies. The system could be used in con

In recent years, a phenomenal increase in the development of Unmanned Aerial Vehicles (UAVs) has been observed in a broad range of applications in various fields of study. Precision agriculture has emerged as a major field of interest, integrating unmanned monitoring of crop health into general agricultural practices for researchers are utilizing UAV to collect data for post-analysis. This paper describes a modular and generic system that is able to control the UAV using computer vision. A configuration approach similar to the Observation, Orientation, Decision and Action (OODA) loop has been implemented to allow the system to perform on-board decision making. The detection of an object of interest is performed by computer vision functionality. This allows the UAV to change its planned path accordingly and approach the target in order to perform a close inspection, or conduct a manoeuvres such as the application of herbicide or collection of higher resolution agricultural images. The results show the ability of the developed system to dynamically change its current goal and implement an inspection manoeuvre to perform necessary actions after detecting the target. The vision based navigation system and on-board decision making were demonstrated in three types of tests: ArUco Marker detection, colour detection and weed detection. The results are measured based on the sensitivity and the selectivity of the algorithm. The sensitivity is the ability of the algorithm to identify and detect the true positive target while the selectivity is the capability of the algorithm to filter out the false negatives for detection targets. Results indicate that the system is capable of detecting ArUco Marker with 99% sensitivity and 100% selectivity at 5 m above the ground level. The system is also capable of detecting a red target with 96% sensitivity and 99% selectivity at the same height during a test height at 5 metres. This system has potential applicab

Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.

Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.

Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.

Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.

Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties. The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior. When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.

Approaches with Vertical Guidance (APV) can provide greater safety and cost savings to general aviation through accurate GPS horizontal and vertical navigation. However, GPS needs augmentation to achieve APV fault detection requirements. Aircraft Based Augmentation Systems (ABAS) fuse GPS with additional sensors at the aircraft. Typical ABAS designs assume high-quality inertial sensors with Kalman filters but these are too expensive for general aviation. Instead of using high-quality (and expensive) sensors, the purpose of this paper is to investigate augmenting GPS with a low-quality MEMS IMU and Aircraft Dynamic Model (ADM). The IMU and ADM are fused together using a multiple model fusion strategy in a bank of Extended Kalman Filters (EKF) with the Normalized Solution Separation (NSS) fault detection scheme. A tightly-coupled configuration with GPS is used and frequent GPS updates are applied to the IMU and ADM to compensate for their errors. Based upon a simulated APV approach, the performance of this architecture in detecting a GPS ramp fault is investigated showing a performance improvement over a GPS-only “snapshot” implementation of the NSS method. The effect of fusing the IMU with the ADM is evaluated by comparing a GPS-IMU-ADM EKF with a GPS-IMU EKF where a small improvement in protection levels is shown.

Sensor fusion deals with the merging of several signals into one, extracting a better and more reliable result. Traditionally the Kalmanfilter is used for this purpose and the aircraft navigation has benefited tremendously from its use. This thesis considers the merge of two navigation systems, the GPS positioning system and the Saab developed Synthetic Attitude and Heading Reference System (SAHRS). The purpose is to find a model for such a fusion and to investigate whether the fusion will improve the overall navigation performance. The non-linear nature of the navigation equations will lead to the use of the extended Kalman filter and the model is evaluated against both simulated and real data. The results show that this strategy indeed works but problems will arise when the GPS signal falls away.

Navigation is about knowing your own position, orientation and velocity relative to some geographic entities. The sensor fusion considered in this thesis combines data from a dead reckoning system, inertial navigation system (INS), and measurements of the ground elevation. The very fast dynamics of aircraft navigation makes it difficult to estimate the true states. Instead the algorithm studied will estimate the errors of the INS and compensate for them. A height database is used along with the measurements. The height database is highly non-linear why a Rao-Blackwellized particle filter is used for the sensor fusion. This integrated navigation system only uses data from its own sensors and from the height database, which means that it is independent of information from outside the aircraft. This report will describe the algorithm and illustrate the theory used. The main purpose is to evaluate the algorithm using real flight data, why the result chapter is the most important.

Sensor fusion deals with the merging of several signals into one, extracting a better and more reliable result. Traditionally the Kalmanfilter is used for this purpose and the aircraft navigation has benefited tremendously from its use. This thesis considers the merge of two navigation systems, the GPS positioning system and the Saab developed Synthetic Attitude and Heading Reference System (SAHRS). The purpose is to find a model for such a fusion and to investigate whether the fusion will improve the overall navigation performance. The non-linear nature of the navigation equations will lead to the use of the extended Kalman filter and the model is evaluated against both simulated and real data. The results show that this strategy indeed works but problems will arise when the GPS signal falls away.

Navigation is about knowing your own position, orientation and velocity relative to some geographic entities. The sensor fusion considered in this thesis combines data from a dead reckoning system, inertial navigation system (INS), and measurements of the ground elevation. The very fast dynamics of aircraft navigation makes it difficult to estimate the true states. Instead the algorithm studied will estimate the errors of the INS and compensate for them. A height database is used along with the measurements. The height database is highly non-linear why a Rao-Blackwellized particle filter is used for the sensor fusion. This integrated navigation system only uses data from its own sensors and from the height database, which means that it is independent of information from outside the aircraft. This report will describe the algorithm and illustrate the theory used. The main purpose is to evaluate the algorithm using real flight data, why the result chapter is the most important.

This paper addresses the problem of integrated aircraft navigation, more specifically how to integrate inertial navigation with terrain aided positioning. This is a highly nonlinear and non-Gaussian recursive state estimation problem which requires state of the art methods. We propose an algorithm based on the particle filter with particular attention to the complexity of the problem. The proposed algorithm takes advantage of linear and Gaussian structure within the system and solves these parts using the Kalman filter. The remaining parts suffering from severe nonlinear and/or non-Gaussian structure are solved using the particle filter. The proposed filter is applied to a simplified integrated navigation system. The result shows that very good performance is achieved for a tractable computational load.

Aerospace Engineering

Aerospace Engineering

In terrain navigation, traditional aircraft navigation sensors are integrated with a digital terrain map. A promising point-mass algorithm has been applied to the problem in previous work. The algorithm recursively updates the probability of the aircraft position in a finite set of positions in a mesh placed over the map area. It has proven efficient in extensive simulations when the grid mesh is chosen sufficiently dense. In order to improve the computational efficiency of the algorithm a novel approach to adaptively choose the grid point positions is presented in this work. Results from wavelet theory and numerical solution of partial differential equations are utilized to adapt the grid mesh on several dyadic scales. Initial tests indicate that the new wavelet filter achieves significant computational performance gains compared to the uniform point-mass version. This fact also makes it applicable to estimation in higer dimension.

The nonlinear estimation problem in navigation using terrain height variations is studied. The optimal Bayesian solution to the problem is derived. The implementation is grid based, calculating the probability of a set of points on an adaptively dense mesh. The Cramer-Rao bound is derived. Monte Carlo simulations over a commercial map shows that the algorithm, after convergence, reaches the Cramer-Rao lower bound.

The majority of aircraft incidents occur during the approach to landing phase of flight. Little research has been conducted that evaluates the efficiency of the instrument display format used by the pilots for the approach to landing. This research examined the effects of two Instrument Landing System display formats on the tracking performance of pilots in a B-IB simulator under varying crosswind and starting conditions. One display contained flight director command steering supplemented with raw glideslope and localizer data; the other display was the same minus the raw data. This research was based on the hypothesis that superior tracking performance would result with flight director and raw glideslope and localizer data on the Instrument Landing System display. The independent variables were: display types, initial starting point, and wind. The dependent variables were: glideslope deviation, localizer deviation, airspeed, roll rate variability, pitch rate variability, and altitude Above Ground Level. Twelve qualified B-1 pilots served as subjects in this simulation study, each subject flew a total of 16 Instrument Handing System approaches after practice. The two types of instrument handing system formats were evaluated under two wind conditions that began with two starting positions.... Image evaluation time, Target acquisition, Automatic target cueing, Radar target acquisition.

A literature search of recent activity to provide an independent indication of aircraft altitude was undertaken. Results indicate that there are several existing techniques in use which can readily be expanded to enhance altitude awareness to avoid inadvertent terrain collisions. Also identified are other new potential candidate systems concepts.

The Military Potential Test of a Doppler Navigator in a Fixed-Wing Aircraft was conducted by USAAVNTBD personnel in the vicinity of Fort Rucker, Alabama, and Tampa, Florida. The test item's weight, power requirments, accuracy, repeatability, and reliability did not meet the requirements of the MC's and SCL 5953. The test item was not suitable for tactical use and was marginally acceptable from a human factors standpoint. It was concluded that the test item is not suitable for Army use and recommended that the Test item be given no further consideration for Army use. The test Doppler navigator is a three-beam, interrupted-CW system operating at 13,325 megacycles. It provides point-to-point navigation from a known base to any number of destinations without use of ground-based navigation aids or visual reference to the ground. The system consists of a receiver-transmitter unit with attached antenna, a frequency-converter tracker, a high-voltage power supply, and a navigation computer with necessary controls and read-out instruments.

The ADF system is a lightweight airborne navigation aid that automatically provides a visual indication of relative bearing of a radio transmitter with respect to the aircraft.

This dissertation presents two contributions to the development of autonomous aerial delivery systems (ADSs), both of which advance the prospect of enabling an ADS to land on a moving platform, such as the deck of a ship at sea. The first contribution addresses the problem of estimating the target’s position and velocity. A novel, dual-rate estimation algorithm based on Unscented Kalman filtering allows the ADS to use visual measurements from a fixed monocular sensor to estimate the target’s motion even when the ADS’s swinging motion in flight causes the target to be out of view. The second contribution addresses the problem of planning a landing trajectory considering winds in the vertical air mass between the target’s height and the ADS’s altitude. A wind model that assumes a logarithmic relationship between horizontal wind velocity and height in the air mass enables the ADS’s guidance algorithm to plan a valid landing trajectory in the presence of these winds. This dissertation contains simulation results for the visual estimation algorithm that show that estimation errors are minimal after estimator convergence. Flight test results indicate that the wind modeling algorithm was useful for computing landing trajectories., http://archive.org/details/posendwindestima1094543926, Commander, United States Navy, Approved for public release; distribution is unlimited.