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.

Today the area of high-cost and high-performance navigation for vehicles is a well-developed field. The challenge now is to develop high-performance navigation systems using low-cost sensortechnology. This development involves problems spanning from signal processing of the dirty measurements produced by low-costsensors via fusion and synchronization of information produced by a large set of diverse sensors, to reducing the size and energyconsumption of the systems. This thesis examines and proposessolutions to four of these problems. The first problem examined is the time synchronizing of the sensordata in a global positioning system aided inertial navigationsystem in which no hardware clock synchronization is possible. A poor time synchronization results in an increased mean squareerror of the navigation solution and expressions for calculating this mean square error are presented. A method to solve the timesynchronization issue in the data integration software is proposed. The potential of the method is illustrated with tests onreal-world data that are subjected to timing errors. The second problem examined is the achievable clocksynchronization accuracy in a sensor network employing a two-waymessage exchange model. The Cramer-Rao bound for the estimation of the clock parameters is derived and transformed in to a lower bound on the mean square error of the clock offset.Further, an approximate maximum likelihood estimator for the clockparameters is proposed. The estimator is shown to be of low complexity and to have a mean square error in the vicinity of the Cramer-Rao bound. The third problem examined is the detection of the time epochswhen zero-velocity updates can be applied in a foot-mountedpedestrian navigation system. Four general likelihood ratio testsfor detecting when the navigation system is stationary based onthe inertial measurement data are studied. The performance of thefour detectors is evaluated using levelled ground, forward-gaitdata. The results show that the signals from the gyroscopes holdthe most reliable information for the zero-velocity detection. The fourth problem examined is the calibration of a low-costinertial measurement unit. A calibration procedure that relaxesthe accuracy requirements of the orientation angles the inertialmeasurement unit must be placed in during the calibration isstudied. The proposed calibration method is compared with theCramer-Rao bound for the case when the inertial measurementunit is rotated into precisely controlled orientations. Simulationresults show that the mean square error of the estimated sensormodel parameters reaches the Cramer-Rao bound within fewdecibels. Thus, the proposed method may be acceptable for a widerange of low-cost applications. / QC 20100810

There has always been a desire to apply technology to human endeavors to increase a person's capabilities and reduce the numbers or skill level required of the people involved, or replace the people altogether. Three fundamental areas are investigated where technology can enable the reduction or removal of humans in complex tasks. The fi rst area of research is the rapid calibration of multiple camera systems when cameras share an overlapping fi eld of view allowing for 3D computer vision applications. A simple method for the rapid calibration of such systems is introduced. The second area of research is the autonomous exploration of hallways or other urbancanyon environments in the absence of a global positions system (GPS) using only an inertial motion unit (IMU) and a monocular camera. Desired paths that generate accurate vehicle state estimates for simple ground vehicles are identi fied and the bene fits of integrated estimation and control are investigated. It is demonstrated that considering estimation accuracy is essential to produce efficient guidance and control. The Schmidt-Kalman filter is applied to the vision-aided inertial navigation system in a novel manner, reducing the state vector size signi ficantly. The final area of research is a decentralized swarm based approach to source localization using a high fidelity environment model to directly provide vehicle updates. The approach is an extension of a standard quadratic model that provides linear updates. The new approach leverages information from the higher-order terms of the environment model showing dramatic improvement over the standard method.

In this thesis, the integration of a MEMS based inertial measurement unit and a three axis solid state magnetometer are studied. It is a fact that unaided inertial navigation systems, especially low cost MEMS based navigation systems have a divergent behavior. Nowadays, many navigation systems use GPS aiding to improve the performance, but GPS may not be applicable in some cases. Also, GPS provides the position and velocity reference whereas the attitude information is extracted through estimation filters. An alternative reference source is a three axis magnetometer, which provides direct attitude measurements. In this study, error propagation equations of an inertial navigation system are derived / measurement equations of magnetometer for Kalman filtering are developed / the unique method to self align the MEMS navigation system is developed. In the motion estimation, the performance of the developed algorithms are compared using a GPS aided system and magnetometer aided system. Some experiments are conducted for self alignment algorithms.

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>

<p>This thesis deals with estimating position and orientation in real-time, using measurements from vision and inertial sensors. A system has been developed to solve this problem in unprepared environments, assuming that a map or scene model is available. Compared to ‘camera-only’ systems, the combination of the complementary sensors yields an accurate and robust system which can handle periods with uninformative or no vision data and reduces the need for high frequency vision updates.</p><p>The system achieves real-time pose estimation by fusing vision and inertial sensors using the framework of nonlinear state estimation for which state space models have been developed. The performance of the system has been evaluated using an augmented reality application where the output from the system is used to superimpose virtual graphics on the live video stream. Furthermore, experiments have been performed where an industrial robot providing ground truth data is used to move the sensor unit. In both cases the system performed well.</p><p>Calibration of the relative position and orientation of the camera and the inertial sensor turn out to be essential for proper operation of the system. A new and easy-to-use algorithm for estimating these has been developed using a gray-box system identification approach. Experimental results show that the algorithm works well in practice.</p>

Methodologies are proposed for combining carrier-phase differential GPS (CDGPS), visual simultaneous localization and mapping (SLAM), and inertial measurements to obtain precise and globally-referenced position and attitude estimates of a rigid structure connecting a GPS receiver, a camera, and an inertial measurement unit (IMU). As part of developing these methodologies, observability of globally-referenced attitude based solely on GPS-based position estimates and visual feature measurements is proven. Determination of attitude in this manner eliminates the need for attitude estimates based on magnetometer and accelerometer measurements, which are notoriously susceptible to magnetic disturbances. This combination of navigation techniques, if coupled properly, is capable of attaining centimeter-level or better absolute positioning and degree-level or better absolute attitude accuracies in any space, both indoors and out. Such a navigation system is ideally suited for application to augmented reality (AR), which often employs a GPS receiver, a camera, and an IMU, and would result in tight registration of virtual elements to the real world. A prototype AR system is presented that represents a first step towards coupling CDGPS, visual SLAM, and inertial navigation. While this prototype AR system does not couple CDGPS and visual SLAM tightly enough to obtain some of the benefit of the proposed methodologies, the system is capable of demonstrating an upper bound on the precision that such a combination of navigation techniques could attain. Test results for the prototype AR system are presented for a dynamic scenario that demonstrate sub-centimeter-level positioning precision and sub-degree-level attitude precision. This level of precision would enable convincing augmented visuals. / text

This thesis project considers the potential use of inertial navigation on a consumer grade tablet mounted in a vehicle in an underground mine. The goal is to identify which sensors and techniques are useful and to design a navigation algorithm based on those results. The navigation algorithm is intended to work alongside the current received signal strength indication (RSSI) positioning system. Testing of the gyroscope, accelerometer and magnetometer sensors suggest that, while dead reckoning is likely not precise enough, an orientation filter can be designed that can be used for navigation. A complementary orientation filter using the gyroscope and accelerometer is then designed that shows better results than the default sensor fusion solutions available in Android. The filter is expandable and can come to include magnetometer data in the future. Based on the outputs of this filter, a navigation algorithm based onangle matching with map information is proposed. Precise positioning in an underground mine can be crucial to employee safety, and may also bring production benefits.

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. 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. 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.