Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation

A modern multisensor integrated navigation system applied in most of civilian applications typically consists of GNSS (Global Navigation Satellite System) receivers, IMUs (Inertial Measurement Unit), and/or other sensors, e.g., odometers and cameras. With the increasing availabilities of low-cost sensors, more research and development activities aim to build a cost-effective system without sacrificing navigational performance. Three principal contributions of this dissertation are as follows: i) A multisensor kinematic positioning and navigation system built on Linux Operating System (OS) with Real Time Application Interface (RTAI), York University Multisensor Integrated System (YUMIS), was designed and realized to integrate GNSS receivers, IMUs, and cameras. YUMIS sets a good example of a low-cost yet high-performance multisensor inertial navigation system and lays the ground work in a practical and economic way for the personnel training in following academic researches. ii) A generic multisensor integration strategy (GMIS) was proposed, which features a) the core system model is developed upon the kinematics of a rigid body; b) all sensor measurements are taken as raw measurement in Kalman filter without differentiation. The essential competitive advantages of GMIS over the conventional error-state based strategies are: 1) the influences of the IMU measurement noises on the final navigation solutions are effectively mitigated because of the increased measurement redundancy upon the angular rate and acceleration of a rigid body; 2) The state and measurement vectors in the estimator with GMIS can be easily expanded to fuse multiple inertial sensors and all other types of measurements, e.g., delta positions; 3) one can directly perform error analysis upon both raw sensor data (measurement noise analysis) and virtual zero-mean process noise measurements (process noise analysis) through the corresponding measurement residuals of the individual measurements and the process noise measurements. iii) The a posteriori variance component estimation (VCE) was innovatively accomplished as an advanced analytical tool in the extended Kalman Filter employed by the GMIS, which makes possible the error analysis of the raw IMU measurements for the very first time, together with the individual independent components in the process noise vector

Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV

Several and distinct Unmanned Aircraft Vehicle (UAV) applications are emerging, demanding steps to be taken in order to allow those platforms to operate in an un-segregated airspace. The key risk component, hindering the widespread integration of UAV in an un-segregated airspace, is the autonomous component: the need for a high level of autonomy in the UAV that guarantees a safe and secure integration in an un-segregated airspace. At this point, the UAV accurate state estimation plays a fundamental role for autonomous UAV, being one of the main responsibilities of the onboard autopilot. Given the 21st century global economic paradigm, academic projects based on inexpensive UAV platforms but on expensive commercial autopilots start to become a non-economic solution. Consequently, there is a pressing need to overcome this problem through, on one hand, the development of navigation systems using the high availability of low cost, low power consumption, and small size navigation sensors offered in the market, and, on the other hand, using Global Navigation Satellite Systems Software Receivers (GNSS SR). Since the performance that is required for several applications in order to allow UAV to fly in an un-segregated airspace is not yet defined, for most UAV academic applications, the navigation system accuracy required should be at least the same as the one provided by the available commercial autopilots. This research focuses on the investigation of the performance of an integrated navigation system composed by a low performance inertial measurement unit (IMU) and a GNSS SR. A strapdown mechanization algorithm, to transform raw inertial data into navigation solution, was developed, implemented and evaluated. To fuse the data provided by the strapdown algorithm with the one provided by the GNSS SR, an Extended Kalman Filter (EKF) was implemented in loose coupled closed-loop architecture, and then evaluated. Moreover, in order to improve the performance of the IMU raw data, the Allan variance and denoise techniques were considered for both studying the IMU error model and improving inertial sensors raw measurements. In order to carry out the study, a starting question was made and then, based on it, eight questions were derived. These eight secondary questions led to five hypotheses, which have been successfully tested along the thesis. This research provides a deliverable to the Project of Research and Technologies on Unmanned Air Vehicles (PITVANT) Group, consisting of a well-documented UAV Development and Validation of an IMU/GPS/Galileo Integration Navigation System for UAV II navigation algorithm, an implemented and evaluated navigation algorithm in the MatLab environment, and Allan variance and denoising algorithms to improve inertial raw data, enabling its full implementation in the existent Portuguese Air Force Academy (PAFA) UAV. The derivable provided by this thesis is the answer to the main research question, in such a way that it implements a step by step procedure on how the Strapdown IMU (SIMU)/GNSS SR should be developed and implemented in order to replace the commercial autopilot. The developed integrated SIMU/GNSS SR solution evaluated, in post-processing mode, through van-test scenario, using real data signals, at the Galileo Test and Development Environment (GATE) test area in Berchtesgaden, Germany, when confronted with the solution provided by the commercial autopilot, proved to be of better quality. Although no centimetre-level of accuracy was obtained for the position and velocity, the results confirm that the integration strategy outperforms the Piccolo system performance, being this the ultimate goal of this research work

Low-cost MEMS-INS/GPS integration using nonlinear filtering approaches

Some important key issues in GNSS/INS integration mainly arise in the field of creating and developing low-cost, robust and at the same time highly accurate navigation systems, putting a focus of interest onto powerful sensor fusion algorithms. The so-called tightly-coupled integration is one of the most promising approaches to fuse the GNSS (global navigation satellite systems) data with INS (inertial navigation system) measurements. However, when modeling the underlying problem, the system process and observation models turn out to be nonlinear, and the GNSS stochastic measurement errors are often non-Gaussian distributed (e.g., due to multipath effects). Among other estimation approaches, the so-called particle filter (PF) as a nonlinear/non-Gaussian estimation method is especially theoretically attractive to be used in this field. However, its large computational burden usually limits its practical usage. In order to reduce the computational burden without degrading the system estimation accuracy, recently, an unscented particle filter (UPF) has been proposed, which combines the PF with the unscented Kalman filter (UKF). In this thesis, only one UKF is used in the algorithm, and the re-sampling step is not required anymore. Thus, the number of particles can be largely reduced, and the implementation of the PF on a hardware platform turns out to be feasible.Aktuelle Entwicklungen auf dem Gebiet der Fusion von inertialer Navigation und satellitengestützten Positionierungsverfahren zielen klar auf kosteneffiziente, robuste und gleichzeitig hochpräzise Lösungen ab. Leistungsfähige Sensordatenfusionsansätze spielen hier eine Schlüsselrolle, wobei die sogenannte "Tightly Coupled Integration" zur Fusion der satellitengestützten Navigationsdaten mit den Messdaten eines inertialen Systems besonders vielversprechend erscheint. Als erschwerender Umstand ergeben sich hier allerdings nichtlineare Prozess- und Beobachtungsmodelle, die in Verbindung mit nicht länger gaußverteilten Beobachtungsfehlern, beispielsweise aufgrund von Mehrwegeausbreitung, nichtlineare, möglichst optimale Datenfusionsverfahren, wie beispielsweise Partikelfilter-Ansätze erfordern. Theoretisch elegant und leistungsfähig auf der einen Seite, benötigen diese Ansätze in der praktischen Realisierung vielfach eine ungemein hohe Anzahl von einzelnen "Partikeln", so dass der hierdurch verursachte Berechnungsaufwand die praktische Einsatzfähigkeit unter Echtzeitbedingungen vielfach entweder im Hinblick auf die Filterperformance oder auf die Taktzeit limitiert. Ein Ansatz zur Lösung dieser Problematik besteht in der Kombination eines Partikelfilters mit einem Unscented Kalman Filter. Hierbei wird der sonst bei Partikelfiltern übliche, aber zeitaufwändige, Resampling Schritt nicht mehr benötigt. Auch die Anzahl der benötigten Partikel kann stark reduziert werden, so dass eine Realisierung auf einer Signalprozessorplattform möglich wird

INS, GPS, and Photogrammetry Integration for Vector Gravimetry Estimation

Presented in Partial Fulfillment of the Requirement for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University.This work was supported by the U.S. Air Force under contract F19628-95-K- 0020 (Defense Mapping Agency funding) and by the National Imagery and Mapping Agency (formerly DMA) under contract NMA202-98-1-1110.Vector gravimetry using Inertial Navigation System (INS) in semi-kinematic mode has been successfully applied. The integration of INS with other sensors, Global Positioning System (GPS) or Gradiometer, for instance, has been under investigation for many years. This dissertation examines the effect of photogrammetric derived orientation on the INS sensor’s calibration and estimation of the gravity vector. The capability of such integration in estimating the INS biases and drifts is studied. The underlying principle, mathematical models, and error sources are presented and analyzed. The estimation process utilizes the measurements of the Litton LN-100 inertial system, Trimble 4000 SSI GPS dual frequency receiver, and metric frame camera. An optimal filtering technique is used to integrate both GPS and INS on the level of raw measurement for both systems. Introducing accurate and independent orientation parameters, e.g., the photogrammetric source in this study, is demonstrated to enable calibration of inertial gyros and bounding of their drift errors. This leads to improvement in the horizontal components of the gravity vector estimation. The estimability and improvement of the deflection of the vertical components are tested using flight test data over Oakland, California, and a set of photogrammetric images simulated along the flight trajectory. The error statistics of the orientation measurement are modeled on the basis of the variance-covariance matrix of a photogrammetric bundle adjustment of all photos. With just a few ground control points at the beginning of the trajectory, the orientation measurement errors along the trajectory are correlated significantly from epoch to epoch, thus reducing the information content of the external orientation estimates. The horizontal gravity component estimation is tested with respect to its sensitivity to the variance of the orientation measurement errors, to its auto-correlation in time, to the cross-correlation between angles, and to the amount of available ground control. Although photogrammetric measurements, if uncorrelated, control orientation errors as well as better than achievable with aircraft maneuvers, the inherent correlation with a very limited amount of ground control provides only a small improvement. On the basis of the simulation parameters, the gravity estimation error was reduced from 20 mgal (GPS/INS only) to about 9 mgal (best uncorrelated control) versus 17 mgal (correlated control)

Development of a Novel Handheld Device for Active Compensation of Physiological Tremor

In microsurgery, the human hand imposes certain limitations in accurately positioning the tip of a device such as scalpel. Any errors in the motion of the hand make microsurgical procedures difficult and involuntary motions such as hand tremors can make some procedures significantly difficult to perform. This is particularly true in the case of vitreoretinal microsurgery. The most familiar source of involuntary motion is physiological tremor. Real-time compensation of tremor is, therefore, necessary to assist surgeons to precisely position and manipulate the tool-tip to accurately perform a microsurgery. In this thesis, a novel handheld device (AID) is described for compensation of physiological tremor in the hand. MEMS-based accelerometers and gyroscopes have been used for sensing the motion of the hand in six degrees of freedom (DOF). An augmented state complementary Kalman filter is used to calculate 2 DOF orientation. An adaptive filtering algorithm, band-limited Multiple Fourier linear combiner (BMFLC), is used to calculate the tremor component in the hand in real-time. Ionic Polymer Metallic Composites (IPMCs) have been used as actuators for deflecting the tool-tip to compensate for the tremor

Contributions to Positioning Methods on Low-Cost Devices

Global Navigation Satellite System (GNSS) receivers are common in modern consumer devices that make use of position information, e.g., smartphones and personal navigation assistants. With a GNSS receiver, a position solution with an accuracy in the order of five meters is usually available if the reception conditions are benign, but the performance degrades rapidly in less favorable environments and, on the other hand, a better accuracy would be beneficial in some applications. This thesis studies advanced methods for processing the measurements of low-cost devices that can be used for improving the positioning performance. The focus is on GNSS receivers and microelectromechanical (MEMS) inertial sensors which have become common in mobile devices such as smartphones. First, methods to compensate for the additive bias of a MEMS gyroscope are investigated. Both physical slewing of the sensor and mathematical modeling of the bias instability process are considered. The use of MEMS inertial sensors for pedestrian navigation indoors is studied in the context of map matching using a particle filter. A high-sensitivity GNSS receiver is used to produce coarse initialization information for the filter to decrease the computational burden without the need to exploit local building infrastructure. Finally, a cycle slip detection scheme for stand-alone single-frequency GNSS receivers is proposed. Experimental results show that even a MEMS gyroscope can reach an accuracy suitable for North seeking if the measurement errors are carefully modeled and eliminated. Furthermore, it is seen that even a relatively coarse initialization can be adequate for long-term indoor navigation without an excessive computational burden if a detailed map is available. The cycle slip detection results suggest that even small cycle slips can be detected with mass-market GNSS receivers, but the detection rate needs to be improved

A Feasibility assessment of a new navigation system for unmanned underwater vehicles with adaptive gain sliding mode differentiation

In this work, a highly accurate navigation device is proposed for unmanned underwater vehicle navigation. A six degree of freedom, open loop underwater vehicle model is generated and is used as the motion platform in this study. The new navigation system, previously developed at the Rochester Institute of Technology, requires real-time body angular acceleration terms as inputs to the algorithm. To address this requirement, real-time signal differentiation techniques were investigated. The differentiation of real-world, noisy signals is a difficult task due to the inherent numerical differentiation and subsequent noise amplification. A sliding mode differentiation scheme is proposed with a fuzzy adaptive controller to aid the accuracy of the signal differentiator and minimize noise amplification. The device algorithms are then implemented in the underwater vehicle model and navigation estimates are compared against theoretical motion. The result is an accurate representation of underwater vehicle attitude and velocity without the aid of global positioning satellite data. Although inertial position estimates obtained from noisy signals suffer from drifting, the filtering techniques used in this work minimize this effect. The navigation estimates show the best results on dynamic maneuvers which do not induce a rolling motion as the underdamped rolling motion requires higher steady state noise for estimation. When assessed against current technologies for underwater vehicle navigation that do not use GPS, the proposed system provides comparable estimation results while creating a reduction of cost, weight and removing the dependence on the speed of sound in water

Robust localization with wearable sensors

Measuring physical movements of humans and understanding human behaviour is useful in a variety of areas and disciplines. Human inertial tracking is a method that can be leveraged for monitoring complex actions that emerge from interactions between human actors and their environment. An accurate estimation of motion trajectories can support new approaches to pedestrian navigation, emergency rescue, athlete management, and medicine. However, tracking with wearable inertial sensors has several problems that need to be overcome, such as the low accuracy of consumer-grade inertial measurement units (IMUs), the error accumulation problem in long-term tracking, and the artefacts generated by movements that are less common. This thesis focusses on measuring human movements with wearable head-mounted sensors to accurately estimate the physical location of a person over time. The research consisted of (i) providing an overview of the current state of research for inertial tracking with wearable sensors, (ii) investigating the performance of new tracking algorithms that combine sensor fusion and data-driven machine learning, (iii) eliminating the effect of random head motion during tracking, (iv) creating robust long-term tracking systems with a Bayesian neural network and sequential Monte Carlo method, and (v) verifying that the system can be applied with changing modes of behaviour, defined as natural transitions from walking to running and vice versa. This research introduces a new system for inertial tracking with head-mounted sensors (which can be placed in, e.g. helmets, caps, or glasses). This technology can be used for long-term positional tracking to explore complex behaviours

Digital route model aided integrated satellite navigation and low-cost inertial sensors for high-performance positioning on the railways

The basis of all railway signalling activities is the knowledge of the position and velocity of all trains in the system. The railways traditionally rely on train detection systems for this knowledge. However, the dependence of these systems on railway infrastructures limits their ability to cope with the advent of new high-speed lines and the development of freight networks across the Europe. Hence, there is a need for the introduction of modern positioning technologies into the railways. Unfortunately railways provide an unfriendly environment for satellite-based radio positioning systems (GNSS). For this reason it is common to integrate GNSS with low-cost inertial sensors (INS) but such systems cannot meet all railway positioning requirements. This thesis examines the potential of enhancing such an integrated GNSS/INS system with a digital route model (DRM). The study is carried out through a series of simulations of typical railway positioning scenes. A simulated database of GNSS, inertial and DRM data is built from real GPS data collected on a rail line between Norwich and Lowestoft. Several tests are first performed to test the validity of the database. Simulations are then done with a number of traditional INS/GPS integration architectures to test the possible performance of each system in the railway environment using lowcost INS sensors. The DRM-aiding is then realised through an integration with the GNNS/INS system via an extended Kalman Filter. Results from the study confirm the need for additional positioning information for an integrated system with low-cost inertial sensors to deal with difficult satellite signal situations such as tunnels, deep cuttings and covered stations. It is shown that a DRM leads to significant improvements in the overall system positioning performance. Also the optimal configuration, in terms of point spacing and accuracy, for a digital route model is selected from amongst simulated candidates