Statistics for Spatial Data.

ITRF2008 is a refined version of the International Terrestrial Reference Frame based on reprocessed solutions of the four space geodetic techniques: VLBI, SLR, GPS and DORIS, spanning 29, 26, 12.5 and 16 years of observations, respectively. The input data used in its elaboration are time series (weekly from satellite techniques and 24-h session-wise from VLBI) of station positions and daily Earth Orientation Parameters (EOPs). The ITRF2008 origin is defined in such a way that it has zero translations and translation rates with respect to the mean Earth center of mass, averaged by the SLR time series. Its scale is defined by nullifying the scale factor and its rate with respect to the mean of VLBI and SLR long-term solutions as obtained by stacking their respective time series. The scale agreement between these two technique solutions is estimated to be 1.05 ± 0.13 ppb at epoch 2005.0 and 0.049 ± 0.010 ppb/yr. The ITRF2008 orientation (at epoch 2005.0) and its rate are aligned to the ITRF2005 using 179 stations of high geodetic quality. An estimate of the origin components from ITRF2008 to ITRF2005 (both origins are defined by SLR) indicates differences at epoch 2005.0, namely: −0.5, −0.9 and −4.7 mm along X, Y and Z-axis, respectively. The translation rate differences between the two frames are zero for Y and Z, while we observe an X-translation rate of 0.3 mm/yr. The estimated formal errors of these parameters are 0.2 mm and 0.2 mm/yr, respectively. The high level of origin agreement between ITRF2008 and ITRF2005 is an indication of an imprecise ITRF2000 origin that exhibits a Z-translation drift of 1.8 mm/yr with respect to ITRF2005. An evaluation of the ITRF2008 origin accuracy based on the level of its agreement with ITRF2005 is believed to be at the level of 1 cm over the time-span of the SLR observations. Considering the level of scale consistency between VLBI and SLR, the ITRF2008 scale accuracy is evaluated to be at the level of 1.2 ppb (8 mm at the equator) over the common time-span of the observations of both techniques. Although the performance of the ITRF2008 is demonstrated to be higher than ITRF2005, future ITRF improvement resides in improving the consistency between local ties in co-location sites and space geodesy estimates.

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ITRF2008: an improved solution of the international terrestrial reference frame

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression

The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

A Wireless Sensor Network (WSN) for Structural Health Monitoring (SHM) is designed, implemented, deployed and tested on the 4200 ft long main span and the south tower of the Golden Gate Bridge (GGB). Ambient structural vibrations are reliably measured at a low cost and without interfering with the operation of the bridge. Requirements that SHM imposes on WSN are identified and new solutions to meet these requirements are proposed and implemented. In the GGB deployment, 64 nodes are distributed over the main span and the tower, collecting ambient vibrations synchronously at 1 kHz rate, with less than 10 mus jitter, and with an accuracy of 30 muG. The sampled data is collected reliably over a 46-hop network, with a bandwidth of 441 B/s at the 46th hop. The collected data agrees with theoretical models and previous studies of the bridge. The deployment is the largest WSN for SHM.

http://glaser.berkeley.edu/glaserdrupal/pdf/EECS-2006-121.pdf

Health monitoring of civil infrastructures using wireless sensor networks

The International GNSS Service (IGS) is an international activity involving more than 200 participating organisations in over 80 countries with a track record of one and a half decades of successful operations. The IGS is a service of the International Association of Geodesy (IAG). It primarily supports scientific research based on highly precise and accurate Earth observations using the technologies of Global Navigation Satellite Systems (GNSS), primarily the US Global Positioning System (GPS). The mission of the IGS is “to provide the highest-quality GNSS data and products in support of the terrestrial reference frame, Earth rotation, Earth observation and research, positioning, navigation and timing and other applications that benefit society”. The IGS will continue to support the IAG’s initiative to coordinate cross-technique global geodesy for the next decade, via the development of the Global Geodetic Observing System (GGOS), which focuses on the needs of global geodesy at the mm-level. IGS activities are fundamental to scientific disciplines related to climate, weather, sea level change, and space weather. The IGS also supports many other applications, including precise navigation, machine automation, and surveying and mapping. This article discusses the IGS Strategic Plan and future directions of the globally-coordinated ~400 station IGS network, tracking data and information products, and outlines the scope of a few of its numerous working groups and pilot projects as the world anticipates a truly multi-system GNSS in the coming decade.

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The International GNSS Service in a changing landscape of Global Navigation Satellite Systems

As well as delivering high speed internet, Wireless LAN (WLAN) can be used as an effective indoor positioning system. It is competitive in terms of both accuracy and cost compared to similar systems. To date, several signal strength based techniques have been proposed. Researchers at the University of New South Wales (UNSW) have developed several innovative implementations of WLAN positioning systems. This paper describes the techniques used and details the experimental results of the research.

/pdf/indoor-positioning-techniques-based-on-wireless-lan-2c0xdeyfpn.pdf

Indoor Positioning Techniques Based on Wireless LAN

The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463307004316

Improving Adaptive Kalman Estimation in GPS/INS Integration

Location fingerprinting in wireless LAN positioning has received much attention recently. One of the key issues of this technique is generating the database of fingerprints. The conventional method does not utilise the spatial correlation of measurements sampled at adjacent reference points, and the training process is not an easy task. A new method based on kriging is presented which can not only achieve more accurate estimation, but can also greatly reduce the workload and save training time. This can make the fingerprinting technique more flexible and easier to implement.

/pdf/method-for-yielding-a-database-of-location-fingerprints-in-1uko1smx96.pdf

Method for yielding a database of location fingerprints in WLAN

The use of magnetic field variations for positioning and navigation has been suggested by several researchers. In most of the applications, the magnetic field is used to determine the azimuth or heading. However, for indoor applications, accurate heading determination is difficult due to the presence of magnetic field anomalies. Here location fingerprinting methodology can take advantage of these anomalies. In fact, the more significant the local anomalies, the more unique the magnetic “fingerprint”. In general, the more elements in each fingerprint, the better for positioning. Unfortunately, magnetic field intensity data only consists of three components. Since true north (or magnetic north) is generally unknown, even with help of the accelerometer to detect the direction of the gravity, only two components can be extracted, i.e. the horizontal intensity and the vertical intensity (or total intensity and inclination). Furthermore, moving objects containing ferromagnetic materials and electronic devices may affect the magnetic field. Tests were carried out to investigate the feasibility of using magnetic field alone for indoor positioning. Possible solutions are discussed.

Chris Rizos

Papers

The International GNSS Service in a changing landscape of Global Navigation Satellite Systems

Indoor Positioning Techniques Based on Wireless LAN

Improving Adaptive Kalman Estimation in GPS/INS Integration

Method for yielding a database of location fingerprints in WLAN

How feasible is the use of magnetic field alone for indoor positioning