När tiden för utveckling av nya produkter kortas ner måste testning och verifiering utföras i ett tidigare utvecklingsstadie. Genom simulering av systemet kan tester utföras utan tillgång till det faktiska systemet och därmed kan utvecklingsprocessen accelereras. I BAE Systems Hägglunds stridsvagnar används en tröghetsnavigator som kan beräkna stridsvagnens position utan externa referenser. Test och verifiering av navigation med denna enhet i testbänk är ej fullt möjligt. Syftet med detta arbete är att kunna genomföra verklighetstrogna tester, i testbänk i utvecklingsfasen, genom att simulera navigatorns funktioner. Eftersom kommunikation med fordonssystemet ska ske i realtid samtidigt som navigationsdata läses från ett externt program, så ställs krav på att modellen har tillräcklig prestanda för att ge en verklighetstrogen simulering. Den övergripande frågeställningen i detta examensarbete är om en modell realiserad på en mikrokontroller (MCU) har tillräcklig prestanda för att användas vid simulering av en tröghetsnavigator. För att besvara frågeställningen har hårdvara för anpassning av gränssnittet mellan fordonssystem, MCU och externt program samt mjukvara för att simulera en tröghetsnavigator skapats. Därefter har modellen verifierats genom att mäta tiden för utvalda processer. Alla funktioner hos navigatorn har inte implementerats i simuleringsmodellen men resultaten visar att modellen kan användas för verklighetstrogna tester i testbänk. / When time for development of new products is shortened, testing and verification must be performed at an earlier stage of development. By simulating the system, tests can be performed without access to the actual system and thus the development process can be accelerated.  BAE Systems Hägglunds manufacture combat vehicles and use an Inertial Navigation System (INS) to calculate the combat vehicle’s position without external references. Testing and verification of navigation with this unit in the test bench is not entirely possible.  The aim of this thesis is to enable realistic tests, in a test bench in the development phase, by simulating the navigator’s functions. Since communication with the Vehicle Control System (VCS) must take place in real time at the same time as navigation data must be read from external program, the model is required to have sufficient performance to provide a realistic simulation.  The overall question in this thesis is whether a model realized on a microcontroller (MCU) has sufficient performance to be used for simulation of an INS. To answer the question at issue, hardware for adapting the interface between the VCS, MCU and external program as well as software for simulating an INS have been created. Thereafter, the model has been verified by measuring the time for selected processes.  Not all functions of the navigator have been implemented in the simulation model, but the results show that the model can be used for realistic tests in the test bench.

A detailed analysis and design of a navigation algorithm for a spacecraft to achieve precision lunar descent and landing is presented. The Inertial Navigation System (INS) was employed as the primary navigation system. To increase the accuracy and precision of the navigation system, the INS was integrated with aiding sensors - a star camera, an altimeter and a terrain camera. An unscented Kalman filter was developed to integrate the aiding sensor measurements with the INS measurements, and to estimate the current position, velocity and attitude of the spacecraft. The errors associated with the accelerometer and gyro measurements are also estimated as part of the navigation filter. An STK scenario was utilized to simulate the truth data for the navigation system. The navigation filter developed was tested and simulated, and from the results obtained, the position, velocity and attitude of the spacecraft were observed to be well estimated.

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Unmanned ground mobile robots are land-based robots which do not have a human passenger on board. They can be either autonomous, or controlled via telecommunication. For navigational purposes, GPS is often used. However, the GPS signal can be distorted in obstructive environments such as tunnels, urban canyons, and dense forests. IMUs can be used to provide an internal navigational solution, free from external input. However, low cost IMUs are prone to various intrinsic sources of error, which leads to large errors in the long run. Using the short term accuracy of the IMU, and the long term accuracy of the GPS, these two technologies are often integrated to combine the aforementioned aspects of the two systems. For integration of the two, various methods are implemented. Such integration methods include Particle Filters, and Kalman Filters. Kalman Filters are commonly used due to their simplicity in calculations. However, the Kalman Filter linearizes the nonlinear error estimates which are inherent with low cost IMUs. The Kalman Filter also does not account for IMU measurement drift, which is present when the measurement unit is used for a long period of time. In this thesis, a Parallel Cascade Identification (PCI) algorithm is augmented with the Kalman Filter (KF) to model the nonlinear errors which are intrinsic to the low cost IMU. The method of integration used was 2D GPS/RISS loosely coupled integration using a Kalman Filter. The PCI algorithm modelled the nonlinear error for the z-axis gyroscope while the GPS signal was available. During a GPS outage, the PCI nonlinear error model was combined with the KF estimated error and the mechanization error, to provide a corrected azimuth. The KFPCI algorithm showed an improvement over the KF algorithm in RMS position error, maximum position error, RMS azimuth error, and maximum azimuth error by an average of 30.76%, 34.71%, 66.76%, and 53.58% in each of the respective areas. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2013-06-11 18:13:12.625

This thesis deals with the possibility of connecting inertial navigation sensors (accelerometer, magnetometer and gyroscope) to determine with the highest precision the position of the user without the help of GPS or other networks. This is inherently connected with the need to deal with many sources of errors, which are connected with this positioning method. The research section describes the principle and history of selected navigation methods and current trends in the use of inertial positioning or navigation methods. The methodical part deals with the design of a system able to determine with the highest accuracy the current position of the user from different input conditions. It is designed to obtain the necessary data from both the sensors and the user and their subsequent processing and use to render the user's position. The application section then describes the practical procedure for creating an Android mobile OS application output and in the discussion part is presented and evaluated the knowledge of testing both during the creation and in the final survey among the test users. The conclusion evaluates the goals and summarizes the practical possibilities and limits of the usability of these positioning methods in mobile phones. It also provides suggestions for further development and...

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.

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.

Due to their complementary characteristics, Global Positioning System (GPS) is usually integrated with standalone navigation devices like odometers and inertial measurement units (IMU). Recently, intensive research has focused on utilizing Micro-Electro-Mechanical-System (MEMS) grade inertial sensors in the integration because of their low cost. In this study, a reduced inertial sensor system (RISS) is considered. It comprises a MEMS grade single axis gyroscope, the vehicle built-in odometer, and two optional MEMS grade accelerometers. Estimation technique is needed to allow the data fusion of RISS and GPS. With adequate accuracy, Kalman filter (KF) fulfills this requirement if high-end inertial sensors are used. However, due to the inherent error characteristics of MEMS devices, MEMS-based RISS suffers from the non-stationary stochastic sensor errors and nonlinear inertial errors, which cannot be suppressed by KF alone. To solve the problem, Fast Orthogonal Search (FOS), a nonlinear system identification algorithm, is suggested in this research for modeling higher order RISS errors. FOS algorithm has the ability to figure out the system nonlinearity with a tolerance of arbitrary stochastic system noise. Its modeling results can then be used to predict the system dynamics. Motivated by the above merits, a KF/FOS module is proposed. By handling both linear and nonlinear RISS errors, this module targets substantial enhancement of positioning accuracy. To examine the effectiveness of the proposed technique, KF/FOS module is applied on RISS with GPS in a land vehicle for several road test trajectories. Its performance is compared to KF-only method, both assessed with respect to a high-end reference. To evaluate navigation algorithm in real-time vehicle application, a multi-sensor data logger is designed in this research to collect online RISS/GPS data. KF/FOS module is transplanted on an embedded digital signal processor as well. Both the off-line and online results confirm that KF/FOS module outperforms KF-only approach in positioning accuracy. They also demonstrate reliable real-time performance. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2012-01-22 01:26:11.477