Particle filter (PF) has been widely used in the target state and position estimations owing to its superiority in tackling the complicated nonlinear problems with arbitrary distributions. As an advanced PF algorithm, Sequential Importance Resampling (SIR) has been widely used for indoor positioning. Since the proposal density of SIR is independent of measurement, the algorithm is vulnerable to outliers. Although the Auxiliary SIR (ASIR) overcomes this problem by performing a two-stage sampling, its positioning accuracy is degraded when the process noise in the filter is large. In order to tackle this problem, an improved ASIR named evolutionary-strategy-integrated ASIR algorithm (EASIR) is proposed in this paper for accuracy improvement in indoor positioning. Tests are carried out for assessing the positioning performance of the proposed algorithm, and positioning accuracy and computation efficiency are considered as the performance metrics in the assessment. Comparing with the SIR and ASIR, the results show that the EASIR achieves better positioning accuracy when the same number of particles are used for data processing and has better robustness when the process noise in the filter is large. Moreover, the computation efficiency of EASIR is generally affordable for real-time applications.

The Global Navigation Satellite Systems (GNSS) receivers often have difficulties within the urban setting. Furthermore, indoors the ordinary receivers do not function. For a short term gaps in positioning fixing measurements, a foot-mounted pedestrian dead reckoning system can offer a replacement solution. This paper presents a novel corrective solution for the gaps and the low-quality GNSS measurements fusion with the pedestrian dead reckoning (PDR). The corrective track form matcher consists of two parts. A short track form matcher compares the PDR track against the GNSS track and infers anomalies in the GNSS track. The short track form matcher tags the GNSS measurements as reliable or unreliable. The long track form matcher uses the reliable GNSS measurements and transforms the PDR track by weighting these good quality measurements more. The accuracy (average error limit of 96%) in the test case improves from 7.56 m to 4.39, this is by 1.72 times.

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required.

An integrated positioning solution termed ‘collaborative positioning’ employs multiple location sensors with different accuracy on different platforms for sharing of their absolute and relative localizations. Typical application scenarios are dismounted soldiers, swarms of UAV's, team of robots, emergency crews and first responders. The stakeholders of the solution (i.e., mobile sensors, users, fixed stations and external databases) are involved in an iterative algorithm to estimate or improve the accuracy of each node's position based on statistical models. This paper studies the challenges to realize a public and low-cost solution, based on mass users of multiple-sensor platforms. For the investigation field experiments revolved around the concept of collaborative navigation, and partially indoor navigation. For this purpose different sensor platforms have been fitted with similar type of sensors, such as geodetic and low-cost high-sensitivity GNSS receivers, tactical grade IMU's, MEMS-based IMU's, miscellaneous sensors, including magnetometers, barometric pressure and step sensors, as well as image sensors, such as digital cameras and Flash LiDAR, and ultra-wide band (UWB) receivers. The employed platforms in the tests include a train on a building roof, mobile mapping vans, a personal navigator and a foot tracker unit. In terms of the tests, the data from the different platforms are recorded simultaneously. Several field experiments conducted in a week at the University of Nottingham are described and investigated in the paper. The personal navigator and a foot tracker unit moved on the building roof, then trough the building down to where it logged data simultaneously with the vans, all of them moving together and relative to each other. The platforms then logged data simultaneously covering various accelerations, dynamics, etc. over longer trajectories. Promising preliminary results of the field experiments showed that a positioning accuracy on the few meter level can be achieved for the navigation of the different platforms.

Foot mounted inertial navigation is an effective method for obtaining high quality pedestrian navigation solutions from MEMS sensors. Zero-Velocity information from stationary periods in the step-cycle can be used to regularly correct position drift and update estimates of the inertial sensor biases, hence dramatically improving the navigation solution. However the causes of heading error remain poorly observable and so foot mounted inertial navigation suffers from considerable drift over time. To address this problem the authors previously developed Cardinal Heading Aided Inertial Navigation (CHAIN). CHAIN makes use of the fact that when in a building, obstacles such as corridors and furniture constrain pedestrians to move in one of four directions parallel to the outside walls of the building. This knowledge is then appropriately weighted and used in an Extended Kalman Filter to improve error estimation. Although the CHAIN method is very effective at improving the quality of the heading estimates, position errors still accumulate with time, and threshold tests are required to cope with periods of motion away from the cardinal headings. In this work we investigate the use of a building floor plan to further aid navigation. This is achieved using a particle filter approach whereby particles which cross walls are removed and those which navigate in open spaces are allowed to continue. Previously the particle filter approach has been computationally intensive process requiring many particles to effectively model the navigation errors. In our work we recognise that heading is the primary source of navigation error and therefore incorporate heuristic heading information into the particle filter design. By weighting particles according to their heading we reduce the number of particles required to maintain a small failure rate and improve system performance in more open areas where there are few mapped walls to aid navigation. This paper will describe the design of our particle filter and the heuristic heading approach. Results from a number of representative test walks using a MEMS IMU will be used to demonstrate the system performance. The use of CHAIN is shown to be capable of significantly reducing the filter failure rate from 44% to 14% when a small number of particles is used in the filter (250) and the initial position is poorly known.

To use heterogeneous and geographically distributed resources as a platform for parallel visualization is an intriguing topic of research. This is because of the immense potential impact of the work, and also because of its use of a full range of challenging technologies. In this work, we designed an execution environment for visualization of massive scientific datasets, using network functional units (NFU) for processing power, logistical networking for storage management and visualization cookbook library (vcblib) for visualization operations. This environment is based solely on computers distributed across the Internet that are owned and operated by independent institutions, while being openly shared for free. Those Internet computers are inherently of heterogeneous hardware configuration and running a variety of operating systems. Using 100 such processors, we have been able to obtain the same level of performance offered by a 64-node cluster of 2.2 GHz P4 processors, while processing a 75GBs subset of a cutting-edge simulation dataset. Due to its inherently shared nature, this execution environment for data-intensive visualization could provide a viable means of collaboration among geographically separated users.

The need to provide remote visualization of large datasets with adequate levels of quality and interactively has become a major impediment to distributed collaboration in Computational Science.Although Image Based Rendering (IBR) techniques based on plenoptic functions have some important advantages over other approaches to this problem, they suffer from an inability to deal with issues of network latency and server load, due to the large size of the IBR databases they generate.Consequently, IBR techniques have been left largely unexplored for this purppose.In this paper we describe strategies for addressing these obstacles using Logistical Networking (LoN), which is a new and highly scalable approach to deploying storage as a shared communication resource.Leveraging LoN technology and infrastructure, we developed a remote visualization system based on concepts of light field rendering, an IBR method using a 4-D plenoptic function.Our system extends existing work on light fields by employing a modified method of parameterization and data organization that supports more efficient prefetching, caching and loss-less compression. Using this approach, we have been able to interactively browse multi-gigabyte, high-resolution light field databases across the wide area network at 30 frames per second.

In this work we present the Internet Backplane Protocol (IBP), a middleware created to allow the sharing of storage resources, implemented as part of the network fabric. IBP allows an application to control intermediate data staging operations explicitly. As IBP follows a very simple philosophy, very similar to the Internet Protocol, and the resulting semantic might be too weak for some applications, we introduce the exNode, a data structure that aggregates storage allocations on the Internet.

In this paper we present the Logistical Networking principles and the tools we developed to support our ideas. Logistical Networking is defined as the global scheduling and optimization of data movement, storage and computation based on a model that takes into account al the network's underlying physical resources. We introduce then the Internet Backplane Protocol (IBP), a middleware created to allow the sharing of storage resources, implemented as part of the network fabric, the exNode, a data structure that aggregates storage allocations on the Internet, and the L-Bone, a directory service of distributed storage resource.

This paper discusses the application of end-to-end design principles, which are characteristic of the architecture of the Internet, to network storage. While putting storage into the network fabric may seem to contradict end-to-end arguments, we try to show not only that there is no contradiction, but also that adherence to such an approach is the key to achieving true scalability of shared network storage. After discussing end-to-end arguments with respect to several properties of network storage, we describe the Internet Backplane Protocol and the exNode, which are tools that have been designed to create a network storage substrate that adheres to these principles. The name for this approach is Logistical Networking, and we believe its use is fundamental to the future of truly scalable communication.

Article Share on Enabling full service surrogates using the portable channel representation Authors: Micah Beck Innovative Computing Laboratory, Department of Computer Science, University of Tennessee, Knoxville, TN Innovative Computing Laboratory, Department of Computer Science, University of Tennessee, Knoxville, TNView Profile , Terry Moore Innovative Computing Laboratory, Department of Computer Science, University of Tennessee, Knoxville, TN Innovative Computing Laboratory, Department of Computer Science, University of Tennessee, Knoxville, TNView Profile , Leif Abrahamsson Lokomo Systems AB, Svärdvägen 27, aSE-182 33 Danderyd, Sweden Lokomo Systems AB, Svärdvägen 27, aSE-182 33 Danderyd, SwedenView Profile , Christophe Achouiantz Lokomo Systems AB, Svärdvägen 27, aSE-182 33 Danderyd, Sweden Lokomo Systems AB, Svärdvägen 27, aSE-182 33 Danderyd, SwedenView Profile , Patrick Johansson Lokomo Systems AB, Svärdvägen 27, aSE-182 33 Danderyd, Sweden Lokomo Systems AB, Svärdvägen 27, aSE-182 33 Danderyd, SwedenView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001Pages 376–385https://doi.org/10.1145/371920.372091Published:01 April 2001Publication History 5citation379DownloadsMetricsTotal Citations5Total Downloads379Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access