337 research outputs found

    Navigation Using Inertial Sensors

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    This tutorial provides an introduction to navigation using inertial sensors, explaining the underlying principles. Topics covered include accelerometer and gyro technology and their characteristics, strapdown inertial navigation, attitude determination, integration and alignment, zero updates, motion constraints, pedestrian dead reckoning using step detection, and fault detection

    Information Aided Navigation: A Review

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    The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.Comment: 8 figures, 3 table

    Inertial Navigation System Data Processing For Position Determination

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    Locating the positions and mapping the spatial information is of critical significance in the field of Precision Farming. Global Positioning System (GPS) is the main tool being utilized for this purpose but it is dependent on the satellite signals. Unfortunately these signals may get lost due to the blockage by canopy of the orchards or plantation. Inertial Navigation System (INS) can address this problem and support the non-availability of GPS signal for a short time. INS is capable of individually calculating the vehicle’s position without any external references. However, its high cost and time dependent errors are its major drawbacks. The research focuses on the mapping solution by INS only so that it can provide solution in the absence of GPS signal. Low cost inertial sensor (Xbow RGA300CA) was used for data collection and processing. Data Processing was done in Matlab/Simulink environment. A Simulink processing model is presented in detail to give an insight of the Strapdown INS Mechanization. Low pass filter and wavelet denoising model was used to assess the margin of improvement for noise filtering. Accurate GPS information was used as a reference of comparison. The model was tested in the lab as well as in the field for its validity. Before going to the field the Inertial sensor was tested in the lab for yaw rate drift and for stationary drift. For kinematic field testing, inertial sensor with GPS was mounted on the vehicle to get the positions for straight trajectories up to 100 meters. Results obtained are presented in detail. A gradual error growth was observed in the INS data and the sensor was found to be stable for short term only. Wavelet denoising was found to be better over short distances up to 40 meters while low pass filtering showed better performance over longer distances up to 100 meters

    Generic Multisensor Integration Strategy and Innovative Error Analysis for Integrated Navigation

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    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

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    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

    Navigation System Design with Application to the Ares I Crew Launch Vehicle and Space Launch Systems

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    For a launch vehicle, the Navigation System is responsible for determining the vehicle state and providing state and state derived information for Guidance and Controls. The accuracy required of the Navigation System by the vehicle is dependent upon the vehicle, vehicle mission, and other consideration, such as impact foot print. NASAs Ares I launch vehicle and SLS are examples of launch vehicles with are/where to employ inertial navigation systems. For an inertial navigation system, the navigation system accuracy is defined by the inertial instrument errors to a degree determined by the method of estimating the initial navigation state. Utilization of GPS aiding greatly reduces the accuracy required in inertial hardware to meet the same accuracy at orbit insertion. For a launch vehicle with lunar bound payload, the navigation accuracy can have large implications on propellant required to correct for state errors during trans-lunar injection

    Performance Analysis of Constrained Loosely Coupled GPS/INS Integration Solutions

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    The paper investigates approaches for loosely coupled GPS/INS integration. Error performance is calculated using a reference trajectory. A performance improvement can be obtained by exploiting additional map information (for example, a road boundary). A constrained solution has been developed and its performance compared with an unconstrained one. The case of GPS outages is also investigated showing how a Kalman filter that operates on the last received GPS position and velocity measurements provides a performance benefit. Results are obtained by means of simulation studies and real dat

    Space Shuttle Navigation in the GPS Era

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    The Space Shuttle navigation architecture was originally designed in the 1970s. A variety of on-board and ground based navigation sensors and computers are used during the ascent, orbit coast, rendezvous, (including proximity operations and docking) and entry flight phases. With the advent of GPS navigation and tightly coupled GPS/INS Units employing strapdown sensors, opportunities to improve and streamline the Shuttle navigation process are being pursued. These improvements can potentially result in increased safety, reliability, and cost savings in maintenance through the replacement of older technologies and elimination of ground support systems (such as Tactical Air Control and Navigation (TACAN), Microwave Landing System (MLS) and ground radar). Selection and missionization of "off the shelf" GPS and GPS/INS units pose a unique challenge since the units in question were not originally designed for the Space Shuttle application. Various options for integrating GPS and GPS/INS units with the existing orbiter avionics system were considered in light of budget constraints, software quality concerns, and schedule limitations. An overview of Shuttle navigation methodology from 1981 to the present is given, along with how GPS and GPS/INS technology will change, or not change, the way Space Shuttle navigation is performed in the 21 5 century
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