Quality control procedures for GNSS precise point positioning in the presence of time correlated residuals

PhD ThesisPrecise point positioning (PPP) is a technique for processing Global Navi- gation Satellite Systems (GNSS) data, often using recursive estimation methods e.g. a Kalman Filter, that can achieve centimetric accuracies using a single receiver. PPP is now the dominant real-time application in o shore marine positioning industry. For high precision real-time applications it is necessary to use high rate orbit and clock corrections in addition to high rate observations. As Kalman filters require input of process and measurement noise statistics, not precisely known in practice, the filter is non-optimal. Geodetic quality control procedures as developed by Baarda in the 1960s are well established and their extension to GNSS is mature. This methodology, largely unchanged since the 1990s, is now being applied to processing techniques that estimate more parameters and utilise many more observations at higher rates. \Detection, Identification and Adaption" (DIA), developed from an optimal filter perspective and utilising Baarda's methodology, is a widely adopted GNSS quality control procedure. DIA utilises various test statistics, which require observation residuals and their variances. Correct derivation of the local test statistic requires residuals at a given epoch to be uncorrelated with those from previous epochs. It is shown that for a non-optimal filter the autocorrelations between observations at successive epochs are non-zero which has implications for proper application of DIA. Whilst less problematic for longer data sampling periods, high rate data using real-time PPP results in significant time correlations between residuals over short periods. It is possible to model time correlations in the residuals as an autoregressive process. Using the autoregressive parameters, the effect of time correlation in the residuals can be removed, creating so-called whitened residuals and their variances. Thus a whitened test statistic can be formed, that satisfies the preferred assumption of uncorrelated residuals over time. The effectiveness of this whitened test statistic and its impact on quality control is evaluated.Fugro Intersite B.V.: The Natural Environment Research Council

Opportunistic timing signals for pervasive mobile localization

Mención Internacional en el título de doctorThe proliferation of handheld devices and the pressing need of location-based services call for precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices. Despite the large investments and efforts in academic and industrial communities, a pin-point solution is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban areas and indoor environments, where the visibility of GNSS satellites is reduced drastically. In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive positioning system should still rely on GNSS, yet complemented with technologies that can guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals are made of data with timing information. We then investigate solutions where opportunistic timing signals can be extracted out of terrestrial technologies. These signals can then be used as additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system that combines range measurements from the Global Positioning System (GPS), the world’s most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques have been recently suggested for ranging mobile devices overWiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments, and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters. The next step we envisioned towards preparing theoretical and practical basis for the aforementioned hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF ranges, and initial foundations of single-technology self-localization. Self-localization systems based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. We study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings. These tools and foundations are necessary to tackle the problem of hybrid positioning system providing high localization capabilities across indoor and outdoor environments. In this context, the lack of a single positioning system that is able the fulfill the specific requirements of diverse indoor and outdoor applications settings has led the development of a multitude of localization technologies. Existing mobile devices such as smartphones therefore commonly rely on a multi-RAT (Radio Access Technology) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is foreseen to provide the highest accuracy in the particular context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies and improve the overall localization accuracy in different contexts. We introduce an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a rich set of multiple ranges can be available across different RATs, the intelligent selection of the subset of ranges with accurate timing information is critical to achieve the best positioning accuracy. We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging measurements. We also address practical problems of the design space, such as removal of WiFi chipset and environmental calibration to make the positioning system as autonomous as possible. Experimental results show that our solution considerably outperforms the use of monolithic technologies and methods based on classical fault detection and identification typically applied in standalone GPS technology. All the contributions and research questions described previously in localization and positioning related topics suppose full knowledge of the anchors positions. In the last part of this work, we study the problem of deriving proximity metrics without any prior knowledge of the positions of the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and respective received signal strength indicator (RSSI) values. Applications that benefit from proximity metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors, we show that metrics from related work perform poorly on real-world data. We analyze the cause for this poor performance, and show that imperfect observations of APs with commodity WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide such proximity estimates, without requiring knowledge of location for the observed AP. We address the challenge of imperfect observations of APs in the design of these improved metrics. Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints. We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en Ingeniería TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior

Low-cost, high-precision, single-frequency GPS–BDS RTK positioning

The integration of the Chinese BDS with other systems, such as the American GPS, makes precise RTK positioning possible with low-cost receivers. We investigate the performance of low-cost ublox receivers, which cost a few hundred USDs, while making use of L1 GPS + B1 BDS data in Dunedin, New Zealand. Comparisons will be made to L1 + L2 GPS and survey-grade receivers which cost several thousand USDs. The least-squares variance component estimation procedure is used to determine the code and phase variances and covariances of the receivers and thus formulate a realistic stochastic model. Otherwise, the ambiguity resolution and hence positioning performance would deteriorate. For the same reasons, the existence of receiver-induced time correlation is also investigated. The low-cost RTK performance is then evaluated by formal and empirical ambiguity success rates and positioning precisions. It will be shown that the code and phase precision of the low-cost receivers can be significantly improved by using survey-grade antennas, since they have better signal reception and multipath suppression abilities in comparison with low-cost patch antennas. It will also be demonstrated that the low-cost receivers can achieve competitive ambiguity resolution and positioning performance to survey-grade dual-frequency GPS receivers

GPS time correlation and its implication for precise navigation

PhD ThesisThe Global Positioning System (GPS) is a satellite navigation system which, when fully operational, "will provide highly accurate position and velocity information in three dimensions, as well as precise time, to users around the globe 24 hours a day" (Anon, 1990). GPS can be operated under all weather conditions, with the only restriction being that the user must be able to receive radio signals from the satellites. Such a comprehensive positioning system has never been previously available, and thus GPS is currently being used for a diverse range of applications. This thesis is focused at the application of GPS for the offshore oil industry which is requiring increasingly higher instantaneous positioning accuracies. The GPS system and real-time positioning techniques are described, along with the main error sources that limit the available accuracy. The suitability of using GPS observations in a standard set of mathematical algorithms, the Kalman filter, in order to obtain position and velocity information has been examined. This is carried out by analysing the observations in order to determine some statistical properties that are usually ignored during the processing and data spanning a two year period has been analysed. The effect that these properties have on the resultant position and its precision was ascertained, finding that position discrepancies were insignificant but their associated precisions were highly dependent on the statistical properties were highly dependent of the data sets. Along similar lines., the ability of the Kalman filter to detect blunders, or gross errors, within GPS-type observations was analysed showing that the relevant test statistics performed sub-optimally and, again, this was dependent on the properties of the data.Shell UK

Robotic Crop Interaction in Agriculture for Soft Fruit Harvesting

Autonomous tree crop harvesting has been a seemingly attainable, but elusive, robotics goal for the past several decades. Limiting grower reliance on uncertain seasonal labour is an economic driver of this, but the ability of robotic systems to treat each plant individually also has environmental benefits, such as reduced emissions and fertiliser use. Over the same time period, effective grasping and manipulation (G&M) solutions to warehouse product handling, and more general robotic interaction, have been demonstrated. Despite research progress in general robotic interaction and harvesting of some specific crop types, a commercially successful robotic harvester has yet to be demonstrated. Most crop varieties, including soft-skinned fruit, have not yet been addressed. Soft fruit, such as plums, present problems for many of the techniques employed for their more robust relatives and require special focus when developing autonomous harvesters. Adapting existing robotics tools and techniques to new fruit types, including soft skinned varieties, is not well explored. This thesis aims to bridge that gap by examining the challenges of autonomous crop interaction for the harvesting of soft fruit. Aspects which are known to be challenging include mixed obstacle planning with both hard and soft obstacles present, poor outdoor sensing conditions, and the lack of proven picking motion strategies. Positioning an actuator for harvesting requires solving these problems and others specific to soft skinned fruit. Doing so effectively means addressing these in the sensing, planning and actuation areas of a robotic system. Such areas are also highly interdependent for grasping and manipulation tasks, so solutions need to be developed at the system level. In this thesis, soft robotics actuators, with simplifying assumptions about hard obstacle planes, are used to solve mixed obstacle planning. Persistent target tracking and filtering is used to overcome challenging object detection conditions, while multiple stages of object detection are applied to refine these initial position estimates. Several picking motions are developed and tested for plums, with varying degrees of effectiveness. These various techniques are integrated into a prototype system which is validated in lab testing and extensive field trials on a commercial plum crop. Key contributions of this thesis include I. The examination of grasping & manipulation tools, algorithms, techniques and challenges for harvesting soft skinned fruit II. Design, development and field-trial evaluation of a harvester prototype to validate these concepts in practice, with specific design studies of the gripper type, object detector architecture and picking motion for this III. Investigation of specific G&M module improvements including: o Application of the autocovariance least squares (ALS) method to noise covariance matrix estimation for visual servoing tasks, where both simulated and real experiments demonstrated a 30% improvement in state estimation error using this technique. o Theory and experimentation showing that a single range measurement is sufficient for disambiguating scene scale in monocular depth estimation for some datasets. o Preliminary investigations of stochastic object completion and sampling for grasping, active perception for visual servoing based harvesting, and multi-stage fruit localisation from RGB-Depth data. Several field trials were carried out with the plum harvesting prototype. Testing on an unmodified commercial plum crop, in all weather conditions, showed promising results with a harvest success rate of 42%. While a significant gap between prototype performance and commercial viability remains, the use of soft robotics with carefully chosen sensing and planning approaches allows for robust grasping & manipulation under challenging conditions, with both hard and soft obstacles

Methods of navigation: An introduction to technological navigation

Ihminen on historian aikana aina navigoinut. Teknologinen navigointi syntyi merenkulussa, koska avomerellä tarvittiin mittauksia oman sijainnin määrittämiseksi. Lentokoneet, ohjukset ja avaruusalukset sekä kuivalla maalla liikkuvat kulkuneuvot ja jopa jalankulkijat kaikki ”navigoivat” nykyteknologioiden avulla. Kehitys on pääosin kahden teknologian ansiota: satelliittipaikannuksen, kuten GPS:n (Global Positioning System), ja inertianavigoinnin. Myös tieto- ja viestintätekniikka on kehittynyt, erityisesti rekursiivinen lineaarinen suodatus eli Kalmanin suodin. Lisäksi pienet ja hinnaltaan huokeat digitaaliset anturit ovat mullistamassa jokapäiväisen navigoinnin. Tässä kirjassa käsiteltäviä aiheita ovat navigoinnin perusteet, stokastiset prosessit, Kalmanin suodin, inertianavigoinnin teknologiat ja menetelmät, GNSS-signaalien rakenne, kantoaallon vaihemittaukset ja kokonaistuntemattomat, tosiaikainen GNSSpaikannus ja navigointi, differentiaalikorjausten viestintäratkaisut ja standardit, GNSStukiasemat ja -verkot, satelliittipohjaiset parannusjärjestelmät, ilmagravimetria sekä anturifuusio ja sattuman anturit.Historically, humankind has always navigated. Technological navigation originated in seafaring, because on the open ocean, measurements are needed in order to determine one’s own location as a part of navigation. Aircraft, rockets and spacecraft as well as vehicles moving on dry land, and even pedestrians, all ”navigate” by means of modern technologies. This development is mainly due to two technologies: satellite positioning, such as GPS (the Global Positioning System) and inertial navigation. Also information and communication technologiy has evolved: especially recursive linear filtering or the Kalman filter. Furthermore, small and inexpensive digital sensors are revolutionising everyday navigation. Subjects explained in this book are the fundamentals of navigation, stochastic processes, the Kalman filter, inertial navigation technology and methods, GNSS signal structure, carrier-phase measurement and ambiguities, real-time GNSS positioning and navigation, communication solutions and standards for differential corrections, GNSS base stations and networks, satellite-based augmentation systems, airborne gravimetry, sensor fusion and sensors of opportunity

ON THE REALISTIC STOCHASTIC MODEL OF GPS OBSERVABLES: IMPLEMENTATION AND PERFORMANCE

High-precision GPS positioning requires a realistic stochastic model of observables. A realistic GPS stochastic model of observables should take into account different variances for different observation types, correlations among different observables, the satellite elevation dependence of observables precision, and the temporal correlation of observables. Least-squares variance component estimation (LS-VCE) is applied to GPS observables using the geometry-based observation model (GBOM). To model the satellite elevation dependent of GPS observables precision, an exponential model depending on the elevation angles of the satellites are also employed. Temporal correlation of the GPS observables is modelled by using a first-order autoregressive noise model. An important step in the high-precision GPS positioning is double difference integer ambiguity resolution (IAR). The fraction or percentage of success among a number of integer ambiguity fixing is called the success rate. A realistic estimation of the GNSS observables covariance matrix plays an important role in the IAR. We consider the ambiguity resolution success rate for two cases, namely a nominal and a realistic stochastic model of the GPS observables using two GPS data sets collected by the Trimble R8 receiver. The results confirm that applying a more realistic stochastic model can significantly improve the IAR success rate on individual frequencies, either on L1 or on L2. An improvement of 20% was achieved to the empirical success rate results. The results also indicate that introducing the realistic stochastic model leads to a larger standard deviation for the baseline components by a factor of about 2.6 on the data sets considered