Missile navigation systems rely on hybrid INS/GPS systems to employ lower grade inertial sensors for the sake of cost and availability. Current inertial navigation systems on missiles can perform accurately for a limited time without GPS aiding. However, GPS is the most likely system that is going to be jammed in a crisis or war by low cost jammers by any opposing force. Missiles do not have adequate equipment to maintain accuracy when GPS is jammed completely in the battle area. In this thesis, a new method is proposed to improve performance of INS systems onboard missiles and autonomous aerial vehicles with EO sensors in a GPS denied environment. Previously laid ground based beacons are used by the missile EO/IIR seeker for bearing-only measurements and position updates are performed by the use of modified artillery survey algorithms based on triangulation techniques which involve angle measurements. For mission planning, two main problems are identified as deployment problem and path planning problem and a tool for the optimal laying of beacons for a given desired trajectory and optimal path planning for a given network of beacons is developed by using evolutionary algorithms and results for test scenarios are discussed.

The objective of this research effort is to provide a methodology for assessing the effectiveness of sampling techniques used to gather different types of geo-physical information by a robotic agent. We focus on assessing how well unique real-time sampling strategies acquire information that is, otherwise, too dangerous or costly to collect by human scientists. Traditional sampling strategies and informed search tech- niques provide the underlying structure for a navigating robotic surveyor whose goal is to collect samples that yield an accurate representation of the measured phenomena under realistic constraints. These sampling strategies are alternative improvements that provide greater information gain than current sampling technology allows. The contributions of this work include the following: 1) A method for estimating spa- tially distributed phenomena, using a partial sample set of information, that shows improvement over that of a more traditional estimation method. 2) A method for sampling this phenomena in the form of a navigation scheme for a mobile robotic survey system. 3) A method of ranking and comparing different navigation algorithms relative to one another based on performance (reconstruction error) and resource (distance) constraints. We introduce a specific class of navigation algorithms as example sampling strategies to demonstrate how our methodology allows different robot navigation options to be contrasted and the most practical strategy selected.

The goal of this thesis is to improve pilot understanding of the Traffic alert and Collision Avoidance System (TCAS) by changing the Traffic Situation Display (TSD). This is supported by two objectives. The first objective is to create an integrated, realistic air traffic environment. This serves as an experimental platform for testing and evaluating future TCAS TSDs. The simulator environment includes a desktop flight simulator, background air traffic simulator, and intruder aircraft. The intruder aircraft uses seven dimensional waypoints to robustly follow trajectories and cause specific resolution advisories. Second, the relative benefits of, and potential concerns with, new TCAS TSDs are explored using a structured, iterative design process with subject matter ex- perts (SMEs). Incremental changes to the TSD were implemented into the simulator environment. SMEs evaluated the displays and potential points of confusion were identified. Several display features are discussed and implemented for future evaluations. These include boundary lines of TCAS variables depicted on the TSD and on a vertical situation display, speed lines which vary with the TCAS estimate of time to closest point of approach, and a prediction of the safe altitude target during a resolution advisory. Scenarios which may be confusing or misleading are discussed. These scenarios may be ameliorated or exacerbated by display features. This information is useful to guide both design and certification or operational approval and is a starting place for future TCAS experiments.

For many years, terrain navigation has been successfully used in military airborne applications. Terrain navigation can essentially improve the performance of traditional inertial-based navigation. The latter is typically built around gyros and accelerometers, measuring the kinetic state changes. Although inertial-based systems benefit from their high independence, they, unfortunately, suffer from increasing error-growth due to accumulation of continuous measurement errors. Undersea, the number of options for navigation support is fairly limited. Still, the navigation accuracy demands on autonomous underwater vehicles are increasing. For many military applications, surfacing to receive a GPS position- update is not an option. Lately, some attention has, instead, shifted towards terrain aided navigation. One fundamental aim of this work has been to show what can be done within the field of terrain aided underwater navigation, using relatively simple means. A concept has been built around a narrow-beam altimeter, measuring the depth directly beneath the vehicle as it moves ahead. To estimate the vehicle location, based on the depth measurements, a particle filter algorithm has been implemented. A number of MATLAB simulations have given a qualitative evaluation of the chosen algorithm. In order to acquire data from actual underwater terrain, a small area of the Swedish lake, Lake Vättern has been charted. Results from simulations made on this data strongly indicate that the particle filter performs surprisingly well, also within areas containing relatively modest terrain variation.

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.

The field of autonomous vehicle navigation and localization is a highly active research topic. The aim of this thesis is to evaluate the feasibility to use outdoor visual navigation in a semi-structured environment. The goal is to develop a visual navigation system for an autonomous golf ball collection vehicle operating on driving ranges. The image feature extractors SIFT and PCA-SIFT was evaluated on an image database consisting of images acquired from 19 outdoor locations over a period of several weeks to allow different environmental conditions. The results from these tests show that SIFT-type feature extractors are able to find and match image features with high accuracy. The results also show that this can be improved further by a combination of a lower nearest neighbour threshold and an outlier rejection method to allow more matches and a higher ratio of correct matches. Outliers were found and rejected by fitting the data to a homography model with the RANSAC robust estimator algorithm. A simulator was developed to evaluate the suggested system with respect to pixel noise from illumination changes, weather and feature position accuracy as well as the distance to features, path shapes and the visual servoing target image (milestone) interval. The system was evaluated on a total of 3 paths, 40 test combinations and 137km driven. The results show that with the relatively simple visual servoing navigation system it is possible to use mono-vision as a sole sensor and navigate semi-structured outdoor environments such as driving ranges.

Observing the relative state of two space vehicles has been an active field of research since the earliest attempts at space rendezvous and docking during the 1960's. Several techniques have successfully been employed by several space agencies and the importance of these systems has been repeatedly demonstrated during the on-orbit assembly and continuous re-supply of the International Space Station. More recent efforts are focused on technologies that can enable fully automated navigation and control of space vehicles. Technologies which have previously been investigated or are actively researched include Video Guidance Systems (VGS), Light Detection and Ranging (LIDAR), RADAR, Differential GPS (DGPS) and Visual Navigation Systems. The proposed system leverages the theoretical foundation which has been advanced in the development of VisNav, invented at Texas A & M University, and the miniaturized commercially available Northstar sensor from Evolution Robotics. The dissertation first surveys contemporary technology, followed by an analytical investigation of the coarse sun sensor and errors associated with utilizing it in the near-field. Next, the commercial Northstar sensor is investigated, utilizing fundamentals to generate a theoretical model of its behavior, followed by the development of an experiment for the purpose of investigating and characterizing the sensor's performance. Experimental results are then presented and compared with a numerical simulation of a single-sensor system performance. A case study evaluating a two sensor implementation is presented evaluating the proposed system's performance in a multisensor configuration. The initial theoretical analysis relied on use of the cosine model, which proved inadequate in fully capturing the response of the coarse sun sensor. Fresenel effects were identified as a significant source of unmodeled sensor behavior and subsequently incorporated into the model. Additionally, near-field effects were studied and modeled. The near-field effects of significance include: unequal incidence angle, unequal incidence power, and non-uniform radiated power. It was found that the sensor displayed inherent instabilities in the 0.3 degree range. However, it was also shown that the sensor could be calibrated to this level. Methods for accomplishing calibration of the sensor in the near-field were introduced and feasibility of achieving better than 1 cm and 1 degree relative position and attitude accuracy in close proximity, even on a small satellite platform, was determined.

GPS is a widely accepted means of navigation, whether it is for civilian or military means. With the implementation of GPS on smart projectiles, these weapons have been able to achieve remarkable accuracy. Even though the improvements in accuracy are impressive, GPS signals are susceptible to jamming and spoofing by a sufficiently motivated enemy. The work reported here examines the viability of constructing a navigation solution using ground based signals of opportunity that provide range and range rate information. Using a generalized sensor model encompassing the key error terms, a variety of physical devices are included in the analysis. For a typical indirect fire trajectory, navigation solutions are computed as a function of the number and density of signal sources, terrain type, and sensor errors. Systematic studies were performed using these parameters in order to better understand the merits and demerits of this type of system to create a useful navigation solution. Based on these studies, results indicate that navigation solutions can be computed with the same accuracy as current GPS systems with a moderate number of signal sources. Generally, more accurate solutions are obtained when the projectile is directly over the signal sources and there is variation of signal source location in all three axes.

<p>In this thesis Sequential Monte Carlo filters, or particle filters, applied to aircraft navigation is considered. This report consists of two parts. The first part is an illustration of the theory behind this thesis project. The second and most important part evaluates the algorithm by using real flight data.</p><p>Navigation is about determining one's own position, orientation and velocity. The sensor fusion studied combines data from an inertial navigation system (INS) with measurements of the ground elevation below in order to form a terrain aided positioning system (TAP). The ground elevation measurements are compared with a height database. The height database is highly non-linear, which is why a marginalized particle filter (MPF) is used for the sensor fusion.</p><p>Tests have shown that the MPF delivers a stable and good estimate of the position, as long as it receives good data. A comparison with Saab's NINS algorithm showed that the two algorithms perform quite similar, although NINS performs better when data is lacking.</p>

Zielsetzung dieser Arbeit ist die Vorstellung des klinischen Aufbaus und des Arbeitsablaufs eines robotischen Assistenzsystems für bildgeführte Interventionen in einem konventionellen Magnetresonanztomographen (MRT), sowie die Beurteilung der Genauigkeit und der klinischen Erfahrungen bei perkutanen Biopsien in verschieden Körperregionen. Material und Methoden: Das MR- kompatible, servopneumatische robotische Assistenzsystem lässt sich mit dem Patienten in die 60- cm Gantry eines Standard- MR- Scanners fahren. Die Genauigkeit des Systems wurde anhand von Nadelpunktionen (n= 25) in einem Phantommodell ermittelt. Perkutane diagnostische Biopsien wurden bei sechs Patienten durchgeführt. Ergebnisse: Für eine Interventionstiefe zwischen 29 und 95 mm wurde eine 3-DGenauigkeit von 2,2 +/- 0,7 mm (Intervall 0,9- 3,8 mm) bestimmt. Patienten mit einem BMI bis zu ≈30 kg/m2 konnten mit dem System punktiert werden. Die klinischen Arbeitsschritte werden anhand der Fallbeispiele dargestellt. Die mittlere Interventionszeit betrug 44 Minuten (Intervall 36 – 68 Minuten). Zusammenfassung: Die Punktion verschiedener Körperregionen ist mit Hilfe des robotischen Assistenzsystems in einem geschlossenen MRT erfolgreich und sicher möglich. Die Genauigkeit des Systems ist vergleichbar mit anderen Assistenzsystemen in der Literatur und genügt den klinischen Anforderungen. Eine kürzere Interventionszeit ist mittels einer Optimierung der einzelnen Arbeitsschritte möglich.