The integrated GPS/INS system has become an indispensable tool for providing precise and continuous position, velocity, and attitude information for many positioning and navigation applications. Although the integrated GPS/INS system provides augmented solutions that make use of the complementary features of each component system, its performance is still limited by the quality of GPS measurements, and the geometric strength of the satellite constellation. To address such a problem this research has focussed on the integration of GPS, Pseudolite and INS technologies. The main research contributions are summarised below: (a)A cost effective GPS/INS integration approach has been developed and tested, consisting of a single-frequency L1 GPS receiver and a tactical-grade strapdown INS. Results of field experiments demonstrate that this approach is capable of delivering position accuracies of the order of a few centimetres under a benign operational environment and provides continuously positioning at sub-decimetre accuracy during GPS signal blockage lasting up to about five seconds. (b) A novel kinematic positioning and navigation system based on GPS/Pseudolite/INS integration has been proposed as an alternative to existing GPS/INS systems. With this integration approach, the continuity, integrity, and precision of the GPS/INS system can be significantly improved as the inclusion of pseudolite signals enhances the GPS signal availability and the geometry strength. (c)The impact of pseudolite location errors in such pseudolite-augmented systems has been investigated. Theoretical and numerical analyses reveal that the error effects on measurement models, and on final positioning solutions, can be minimised by selecting optimal pseudolite location(s). (d)A new ambiguity resolution procedure has been developed for use in the proposed GPS/Pseudolite/INS system. It is designed to rapidly and reliably resolve the single-frequency ambiguities due not only to the aiding by pseudolites and INS, but also by adopting a realistic stochastic model and a statistically rigorous ambiguity validation test. The proposed procedure can indeed improve the performance of the single-frequency ambiguity resolution algorithm in terms of both reliability and time-to-fix-ambiguity. (e)An effective cycle slip detection and identification algorithm has been developed, which is suitable for the integrated GPS/Pseudolite/INS system. Test results indicate that induced cycle slips can be reliably detected and instantaneously identified, even if the slips occur at successive epochs. (f)Flight trials have been conducted to evaluate the overall performance for aircraft approach and landing using the GPS/Pseudolite/INS system. Results from these trials show that an enhancement in the accuracy and reliability of the vehicle navigation solution can be achieved with the employment of one or more pseudolite.

No description available.

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / Map matching method plays an important role in vehicle location and navigation systems. It employs the information in a digital map to compensate the positioning error. This paper presents a fuzzy-logic-based probabilistic map-matching algorithm used in GPS/INS integrated navigation systems, in which the reliability degree of map matching resolution is given explicitly as the decision basis in selecting matching road segment by utilizing the fuzzy comprehensive judgement. The results of experimental simulations have shown that the system performance gained significant enhancement by introducing this algorithm.

<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>

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.

This thesis describes the theoretical and practical stages through development to testing of an integrated navigation system, specifically composed of an Inertial Navigation System (INS), and Global Positioning System (GPS). Integrated navigation systems combine the best features of independent systems to bring out increased performance, improved reliability and system integrity. In an integrated INS/GPS system, INS output is used to calculate current navigation states / GPS output is used to supply external measurements, and a Kalman filter is used to provide the most probable corrections to the state estimate using both data. Among various INS/GPS integration strategies, our aim is to construct a tightly coupled integrated INS/GPS system. For this purpose, mathematical models of INS and GPS systems are derived and they are linearized to form system dynamics and system measurement models respectively. A Kalman filter is designed and implemented depending upon these models. Besides these, based on the given aided navigation system representation a quantitative measure for observability is defined using Gramians. Finally, the performance of the developed system is evaluated with real data recorded by the sensors. A comparison with a reference system and also with a loosely coupled system is done to show the superiority of the tightly coupled structure. Scenarios simulating various GPS data outages proved that the tightly coupled system outperformed the loosely coupled system from the aspects of accuracy, reliability and level of observability.

<p>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. </p><p>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.</p>

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.

Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownship inertial position, inertial velocity, and attitude) that are directly used in the autopilot system, and the tracking system provides low-rate target tracking states (typically target relative position and velocity with respect to the ownship). Target states in the global frame can be obtained by adding the ownship states and the target tracking states. The data estimated from this combination of the navigation system and the tracking system provide key information for the design of most UAV guidance laws, control command generation, trajectory generation, and path planning. As a baseline system that estimates ownship states, an integrated navigation system is designed by using an extended Kalman filter (EKF) with sequential measurement updates. In order to effectively fuse various sources of aiding sensor information, the sequential measurement update algorithm is introduced in the design of the integrated navigation system with the objective of being implemented in low-cost autonomous UAV's. Since estimated state accuracy using a low-cost, MEMS-based IMU degrades with time, several absolute (low update rate but bounded error in time) sensors, including the GPS receiver, the magnetometer, and the altimeter, can compensate for time-degrading errors. In this work, the sequential measurement update algorithm in smaller vectors and matrices is capable of providing a convenient framework for fusing the many sources of information in the design of integrated navigation systems. In this framework, several aiding sensor measurements with different size and update rates are easily fused with basic high-rate IMU processing. In order to provide a new mechanism that estimates ownship states, a new nonlinear filtering framework, called the unscented Kalman filter (UKF) with sequential measurement updates, is developed and applied to the design of a new integrated navigation system. The UKF is known to be more accurate and convenient to use with a slightly higher computational cost. This filter provides at least second-order accuracy by approximating Gaussian distributions rather than arbitrary nonlinear functions. This is compared to the first-order accuracy of the well-known EKF based on linearization. In addition, the step of computing the often troublesome Jacobian matrices, always required in the design of an integrated navigation system using the EKF, is eliminated. Furthermore, by employing the concept of sequential measurement updates in the UKF, we can add the advantages of sequential measurement update strategy such as easy compensation of sensor latency, easy fusion of multi-sensors, and easy addition and subtraction of new sensors while maintaining those of the standard UKF such as accurate estimation and removal of Jacobian matrices. Simulation results show better performance of the UKF-based navigation system than the EKF-based system since the UKF-based system is more robust to initial accelerometer and rate gyro biases and more accurate in terms of reducing transient peaks and steady-state errors in ownship state estimation. In order to estimate target tracking states or target kinematics, a new vision-based tracking system is designed by using a UKF in the scenario of three-dimensional air-to-air tracking. The tracking system can estimate not only the target tracking states but also several target characteristics including target size and acceleration. By introducing the UKF, the new vision-based tracking system presents good estimation performance by overcoming the highly nonlinear characteristics of the problem with a relatively simplified formulation. Moreover, the computational step of messy Jacobian matrices involved in the target acceleration dynamics and angular measurements is removed. A new particle filtering framework, called an extended marginalized particle filter (EMPF), is developed and applied to the design of a new vision-based tracking system. In this work, only three position components with vision measurements are solved in particle filtering part by applying Rao-Blackwellization or marginalization approach, and the other dynamics, including the target nonlinear acceleration model, with Gaussian noise are effectively handled by using the UKF. Since vision information can be better represented by probabilistic measurements and the EMPF framework can be easily extended to handle this type of measurements, better performance in estimating target tracking states will be achieved by directly incorporating non-Gaussian, probabilistic vision information as the measurement inputs to the vision-based tracking system in the EMPF framework.

Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) Integrated Navigation Systems (INS) integrate the positive features of GNSS and IMU for optimal navigation guidance in high accuracy outdoor navigation systems, for example using Extended Kalman Filter (EKF) techniques. Time synchronization of IMU data with precise GNSS based time is necessary to accurately synchronize the two systems. This must be done in real-time for time sensitive navigation applications such as autonomous vehicles. The research is done in two parts. The first part is the simulation of inaccurate time-stamping in a single axis of nonlinear input data in a gyroscope and an accelerometer, to obtain the timing error value that is tolerable by a high accuracy GNSS/INS system. The second part is the creation of a real-time algorithm using an STM32 embedded system enabled with FreeRTOS real-time kernel for a GNSS receiver and antenna, along with an IMU sensor. A comparative analysis of the time synchronized system and an unsynchronized system is done based on the errors produced using gyroscope and accelerometer readings along a single axis from the IMU sensor, by conducting static and rotational tests on a revolving chair.The simulation concludes that a high accuracy GNSS/INS system can tolerate a timing error of up to 1 millisecond. The real-time solution provides IMU data paired with updated GNSS based time-stamps every 5 milliseconds. The timing jitter is reduced to a range of ±1 millisecond. Analysis of final angular rotation error and final position error from gyroscope and accelerometer readings respectively, indicate that the real-time algorithm produces a reduction in errors when the system is static, but there is no statistical evidence showing the reduction of errors from the results of the rotational tests. / GNSS / IMU integrerade navigationssystem kombinerar de positiva egenskaperna hos GNSS och IMU för optimal prestanda i noggranna navigationssystem. Detta görs med hjälp av sensorfusion, till exempel EKF. Tidssynkronisering av IMU-data med exakt GNSS-baserad tid är nödvändigt för att noggrant synkronisera de två systemen. Detta måste göras i realtid för tidskänsliga navigationsapplikationer såsom autonoma fordon. Forskningen görs i två delar. Den första delen är simulering av icke-linjär rörelse i en axel med felaktig tidsstämpling hos ett gyroskop och en accelerometer. Detta görs för att erhålla det högsta tidsfel som är acceptabelt hos ett GNSS / INS-system med hög noggrannhet. Den andra delen är skapandet av en realtidsalgoritm med ett inbyggt STM32-system med FreeRTOS som realtidskärna för en GNSSmottagare och antenn, tillsammans med en IMU-sensor. En jämförande analys av det tidssynkroniserade systemet mot ett osynkroniserat system görs baserat på de positionsfel längs en axel som produceras av gyroskopoch accelerometermätningar. Detta görs genom att utföra statiska och roterande tester med hjälp av en roterande stol.Simuleringen visar att ett noggrant GNSS / INS-system tolererar ett tidsfel på upp till 1 millisekund. Realtidslösningen ger IMU-data med tidsstämplar synkroniserade med GNSS-tid var femte millisekund. Tidsjittret reduceras till ett intervall mellan ± 1 millisekund. Analysen av det slutliga vinkelrotationsfelet och positionsfelet från gyroskopoch accelerometermätningar indikerar att realtidsalgoritmen ger ett lägre fel när systemet är statiskt. Det finns dock inga statistiska bevis för förbättringen från resultaten av rotationstesterna.