Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

An Unmanned Aerial Vehicle-Based Imaging System for 3D Measurement of Unpaved Road Surface Distresses1

Abstract: :i¾?Road condition data are important in transportation management systems. Over the last decades, significant progress has been made and new approaches have been proposed for efficient collection of pavement condition data. However, the assessment of unpaved road conditions has been rarely addressed in transportation research. Unpaved roads constitute approximately 40% of the U.S. road network, and are the lifeline in rural areas. Thus, it is important for timely identification and rectification of deformation on such roads. This article introduces an innovative Unmanned Aerial Vehicle UAV-based digital imaging system focusing on efficient collection of surface condition data over rural roads. In contrast to other approaches, aerial assessment is proposed by exploring aerial imagery acquired from an unpiloted platform to derive a three-dimensional 3D surface model over a road distress area for distress measurement. The system consists of a low-cost model helicopter equipped with a digital camera, a Global Positioning System GPS receiver and an Inertial Navigation System INS, and a geomagnetic sensor. A set of image processing algorithms has been developed for precise orientation of the acquired images, and generation of 3D road surface models and orthoimages, which allows for accurate measurement of the size and the dimension of the road surface distresses. The developed system has been tested over several test sites with roads of various surface distresses. The experiments show that the system is capable for providing 3D information of surface distresses for road condition assessment. Experiment results demonstrate that the system is very promising and provides high accuracy and reliable results. Evaluation of the system using 2D and 3D models with known dimensions shows that subcentimeter measurement accuracy is readily achieved. The comparison of the derived 3D information with the onsite manual measurements of the road distresses reveals differences of 0.50 cm, demonstrating the potential of the presented system for future practice.

Navigation: Principles of Positioning and Guidance

Abstract: From the Table of Contents: Abbreviations and acronyms Introduction Historical review Mathematical fundamentals Physical fundamentals Maps Terrestrial navigation Celestial navigation Terrestrial radio navigation Satellite-based navigation Augmentation systems Inertial navigation Image-based navigation Integrated navigation Routing and guidance Vehicle and traffic management Application examples Critical outlook References Index

Decentralized Filtering and Redundancy Management for Multisensor Navigation

Abstract: Failure detection and redundancy management is discussed for avionics applications of integrated navigation involving coordinated use of multiple simultaneous sensor subsystems such as GPS, JTIDS, TACAN, VOR/DME, ILS, an inertial navigation system (INS), and possibly even Doppler AHRS. A brief high level survey is provided to assess the status of those techniques and methodologies advertized as already available for handling the challenging real-time failure detection, redundancy management, and Kalman filtering aspects of these systems with differing availabilities, differing reliabilities, differing accuracies, and differing information content/sampling rates. Following the status review, a new failure detection/redundancy management approach is developed based on voter/monitoring at both the raw data and at the filtered-data level, as well as using additional inputs from hardware built-in-testing (BIT) and from specialized tests for subsequent failure isolation in the case of ambiguous indications. The technique developed involves use of Gaussian confidence regions to reasonably account for the inherent differences in accuracy between the various sensor subsystems. Online estimates of covariances from the Kalman filter are to be used for this purpose (when available). A technique is provided for quantitatively evaluating both the probability of detecting failed component subsystems and the probability of false alarm to be incurred, which is then to be traded off as the basis for rational selection of the thresholds used in the automated decision process. Moreover, the redundancy management procedure is demonstrated to be amenable to pilot or navigation operator prompting and override, if necessary.

Evaluation of Two WiFi Positioning Systems Based on Autonomous Crowdsourcing of Handheld Devices for Indoor Navigation

Abstract: Current WiFi positioning systems (WPSs) require databases – such as locations of WiFi access points and propagation parameters, or a radio map – to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of automatically generated databases, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors’ solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.

Positioning and proximity warning method and system thereof for vehicle

Abstract: A positioning and proximity warning method for vehicle includes the steps of outputting global positioning system signals to an integrated positioning/ground proximity warning system processor; outputting an inertial navigation solution to an integrated positioning/ground proximity warning processor; measuring air pressure, and computing barometric measurements which is output to the integrated positioning/ground proximity warning processor; measuring time delay between transmission and reception a radio signal from a terrain surface, and computing radio altitude measurement which is output to the integrated positioning/ground proximity warning processor; accessing a terrain database for obtaining current vehicle position and surrounding terrain height data which is output to the integrated positioning/ground proximity warning processor; and receiving the position, velocity and time information or said pseudorange and delta range measurements of said global positioning system, the inertial navigation solution, the radio altitude measurement, the radio altitude measurement, and the current vehicle position and surrounding terrain height data, and computing optimal positioning solution data and optimal ground proximity warning solution data. Furthermore, a positioning and proximity warning method for vehicles further includes the steps of outputting the optimal positioning solution data and position data of near objects to an object tracking and collision avoidance processor to determine a potential collision threat with the near object.