Book•
Principles of GNSS, Inertial, and Multi-Sensor Integrated Navigation Systems
31 Dec 2007-
TL;DR: In this paper, the authors present a single-source reference for navigation systems engineering, providing both an introduction to overall systems operation and an in-depth treatment of architecture, design, and component integration.
Abstract: Navigation systems engineering is a red-hot area. More and more technical professionals are entering the field and looking for practical, up-to-date engineering know-how. This single-source reference answers the call, providing both an introduction to overall systems operation and an in-depth treatment of architecture, design, and component integration. This book explains how satellite, on-board, and other navigation technologies operate, and it gives practitioners insight into performance issues such as processing chains and error sources. Providing solutions to systems designers and engineers, the book describes and compares different integration architectures, and explains how to diagnose errors. Moreover, this hands-on book includes appendices filled with terminology and equations for quick referencing.
Citations
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21 Sep 2008
TL;DR: This paper looks at how a foot-mounted inertial unit, a detailed building model, and a particle filter can be combined to provide absolute positioning, despite the presence of drift in the inertial units and without knowledge of the user's initial location.
Abstract: Location information is an important source of context for ubiquitous computing systems. This paper looks at how a foot-mounted inertial unit, a detailed building model, and a particle filter can be combined to provide absolute positioning, despite the presence of drift in the inertial unit and without knowledge of the user's initial location. We show how to handle multiple floors and stairways, how to handle symmetry in the environment, and how to initialise the localisation algorithm using WiFi signal strength to reduce initial complexity.We evaluate the entire system experimentally, using an independent tracking system for ground truth. Our results show that we can track a user throughout a 8725 m2 building spanning three floors to within 0.5m 75% of the time, and to within 0.73 m 95% of the time.
563Â citations
Cites background or methods from "Principles of GNSS, Inertial, and M..."
...These include the federated zero-reset architecture, in which the state of the first filter is zeroed following input to the second filter, with the corresponding elements of the first filter’s covariance matrix reset to their initialisation values [18]....
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...The spectrum of such noise is approximately white for frequencies below 1 Hz, meaning that the standard deviation of the average angular velocity noise is inversely proportional to the square root of the averaging time [18]....
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...The lack of a single silver bullet technology for indoor positioning has led to a more recent shift towards the development of indoor positioning systems that use multiple sensor types (an approach that has been used for outdoor positioning since the 1960s and is commonly known as integrated navigation [18])....
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...With a clear view of the sky and when tracking all visible satellites in the GPS constellation, 1 position errors of approximately 7 m can be achieved by single frequency GPS receivers [18]....
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...More accurate (but more processor intensive) approaches include modelling bias instability using Markov processes and representing sensor biases using second-order and third-order autoregressive models [18]....
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01 Jan 2012
TL;DR: This article provides a simple and intuitive derivation of the Kalman filter, with the aim of teaching this useful tool to students from disciplines that do not require a strong mathematical background.
Abstract: T his article provides a simple and intuitive derivation of the Kalman filter, with the aim of teaching this useful tool to students from disciplines that do not require a strong mathematical background. The most complicated level of mathematics required to understand this derivation is the ability to multiply two Gaussian functions together and reduce the result to a compact form. The Kalman filter is over 50 years old but is still one of the most important and common data fusion algorithms in use today. Named after Rudolf E. Kalman, the great success of the Kalman filter is due to its small computational requirement, elegant recursive properties, and its status as the optimal estimator for one-dimensional linear systems with Gaussian error statistics [1] . Typical uses of the Kalman filter include smoothing noisy data and providing estimates of parameters of interest. Applications include global positioning system receivers, phaselocked loops in radio equipment, smoothing the output from laptop trackpads, and many more. From a theoretical standpoint, the Kalman filter is an algorithm permitting exact inference in a linear dynamical system, which is a Bayesian model similar to a hidden Markov model but where the state space of the latent variables is continuous and where all latent and observed variables have a Gaussian distribution (often a multivariate Gaussian distribution). The aim of this lecture note is to permit people who find this description confusing or terrifying to understand the basis of the Kalman filter via a simple and intuitive derivation.
379Â citations
TL;DR: This work proposes a sensor fusion framework for combining WiFi, PDR and landmarks, and can provide an average localization accuracy of 1 m, which shows significant improvement using the proposed framework.
Abstract: Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.
360Â citations
Cites background from "Principles of GNSS, Inertial, and M..."
...Alternatively, the authors in [25] presented a linear relationship between step length and the height of the pedestrian, expressed as:...
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TL;DR: In this article, the authors used 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position.
Abstract: The Global Positioning System (GPS) is unreliable in dense urban areas, known as urban canyons, which have tall buildings or narrow streets. This is because the buildings block the signals from many of the satellites. Combining GPS with other Global Navigation Satellite Systems (GNSS) significantly increases the availability of direct line-of-sight signals. Modelling is used to demonstrate that, although this will enable accurate positioning along the direction of the street, the positioning accuracy in the cross-street direction will be poor because the unobstructed satellite signals travel along the street, rather than across it. A novel solution to this problem is to use 3D building models to improve cross-track positioning accuracy in urban canyons by predicting which satellites are visible from different locations and comparing this with the measured satellite visibility to determine position. Modelling is used to show that this shadow matching technique has the potential to achieve metre-order cross-street positioning in urban canyons. The issues to be addressed in developing a robust and practical shadow matching positioning system are then discussed and solutions proposed.
273Â citations
Cites background or methods from "Principles of GNSS, Inertial, and M..."
...Along-street and crossstreet DOP were then calculated using the conventional method [9, 10]....
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...These compare quantities calculated from different combinations of measurements to determine whether they are consistent [9]....
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...Finally, dead reckoning [9] could be used to bridge a navigation solution between intersections, though the accuracy will depend on sensor quality and environment....
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...The UERE estimation was based on the following assumptions [9] :...
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TL;DR: An overview of the past and current literature discussing the GNSS integrity for urban transport applications is provided so as to point out possible challenges faced by GNSS receivers in such scenario.
Abstract: Integrity is one criteria to evaluate GNSS performance, which was first introduced in the aviation field. It is a measure of trust which can be placed in the correctness of the information supplied by the total system. In recent years, many GNSS-based applications emerge in the urban environment including liability critical ones, so the concept of integrity attracts more and more attention from urban GNSS users. However, the algorithms developed for the aerospace domain cannot be introduced directly to the GNSS land applications. This is because a high data redundancy exists in the aviation domain and the hypothesis that only one failure occurs at a time is made, which is not the case for the urban users. The main objective of this paper is to provide an overview of the past and current literature discussing the GNSS integrity for urban transport applications so as to point out possible challenges faced by GNSS receivers in such scenario. Key differences between integrity monitoring scheme in aviation domain and urban transport field are addressed. And this paper also points out several open research issues in this field.
265Â citations
References
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Book•
01 Dec 2010TL;DR: This advanced tutorial will describe the GPS signals, the various measurements made by the GPS receivers, and estimate the achievable accuracies, and focus on topics which are more unique to radio navigation or GPS.
Abstract: The Global Positioning System (GPS) is a satellite-based navigation and time transfer system developed by the U.S. Department of Defense. It serves marine, airborne, and terrestrial users, both military and civilian. Specifically, GPS includes the Standard Positioning Service (SPS) which provides civilian users with 100 meter accuracy, and it serves military users with the Precise Positioning Service (PPS) which provides 20-m accuracy. Both of these services are available worldwide with no requirement for a local reference station. In contrast, differential operation of GPS provides 2- to 10-m accuracy to users within 1000 km of a fixed GPS reference receiver. Finally, carrier phase comparisons can be used to provide centimeter accuracy to users within 10 km and potentially within 100 km of a reference receiver. This advanced tutorial will describe the GPS signals, the various measurements made by the GPS receivers, and estimate the achievable accuracies. It will not dwell on those aspects of GPS which are well known to those skilled in the radio communications art, such as spread-spectrum or code division multiple access. Rather, it will focus on topics which are more unique to radio navigation or GPS. These include code-carrier divergence, codeless tracking, carrier aiding, and narrow correlator spacing.
2,203Â citations
Book•
01 Jan 1999
TL;DR: The Science of Navigation.
Abstract: The Science of Navigation. Coordinate Frames and Transformations. Systems Concepts. Discrete Linear and Non-Linear Kalman Filtering Techniques. The Global Positioning System. Inertial Navigation. Navigation Examples and Case Studies. Appendices: A: Notation, Symbols, and Constants. B: Matrix Review.
906Â citations
01 Jan 2001
401Â citations
01 Jan 1995
TL;DR: Shows exceptional skills and knowledge on the job, strong understanding of all aspects of department, and minimal knowledge of the essentials.
Abstract: Shows exceptional skills and knowledge on the job. Has strong understanding of all aspects of department. Very well informed on all phases of the position. Requires little or no supervision. Has a good understanding of all aspects of job. Requires standard supervision. Has minimal knowledge of the essentials. Needs close supervision Unacceptable job knowledge. Requires maximum supervision in most or all areas of job responsibilities.
269Â citations