Sturdy Positioning with High Sensitivity GPS Sensors Under Adverse Conditions

High sensitivity GPS receivers have extended the use of GNSS navigation to environments which were previously deemed unsuitable for satellite signal reception. Under adverse conditions the signals become attenuated and reflected. High sensitivity receivers achieve signal reception by using a large number of correlators and an extended integration time. Processing the observation data in dynamic and rapidly changing conditions requires a careful and consistent treatment. Code-based autonomous solutions can cause major errors in the estimated position, due primarily to multipath effects. A custom procedure of autonomous GPS positioning has been developed, boosting the positioning performance through appropriate processing of code and Doppler observations. Besides the common positioning procedures, robust estimation methods have been used to minimise the effects of gross observation errors. In normal conditions, differential GNSS yields good results, however, under adverse conditions, it fails to improve significantly the receiver’s position. Therefore, a so-called conditional DGPS has been developed which determines the position differentially by using data from the strong signals only. These custom-developed procedures have been tested in different conditions in static and kinematic cases and the results have been compared to those processed by the receiver

An Autonomous Waist-Mounted Pedestrian Dead Reckoning System by Coupling Low-Cost MEMS Inertial Sensors and GPS Receiver for 3D Urban Navigation

Global positioning system (GPS) offers a perfect solution to the 3-dimension(3D) navigation. However, the GPS-only solution can’t provide continuous and accurate position information in the unfavourable environments, such as urban canyons, indoor buildings, dense foliages due to signal blockage, interference, or jamming etc. A pedestrian dead reckoning (PDR) system integrating the self-contained inertial sensors with GPS receiver is proposed to provide a seamless outdoor/indoor 3D pedestrian navigation. The MEM sensor module attached to the user’s waist is composed of a 3-axis accelerometer, a 3-axis gyroscope, a 3-axis digital compass and a barometric pressure sensor, which doesn’t rely on any infrastructure. The positioning algorithm implements a loosely coupled GPS/PDR integration. The sensor data are fused via a complementary filter to reduce the integral drift and magnetic disturbance for accurate heading. The four key components of the PDR algorithm: step detection, stride length estimation, heading and position determination are described in detail and implemented by the microcontroller. The step is detected using the accelerometer signals by the combination of three approaches: sliding window, peak detection and zero-crossing. The step length is estimated using a simple linear relationship with the step frequency. By coupling the step length, azimuth and height, 3D navigation is achieved. The performance of the proposed system is carefully verified through several field outdoor and indoor walking tests. The positioning errors are below 3% of the total traveled distance. The main error source comes from the orientation estimation. The results indicate that the proposed system is effective in accurate tracking

Optimizing the Sampling Area across an Old-Growth Forest via UAV-Borne Laser Scanning, GNSS, and Radial Surveying

Aboveground biomass, volume, and basal area are among the most important structural attributes in forestry. Direct measurements are cost-intensive and time-consuming, especially for old-growth forests exhibiting a complex structure over a rugged topography. We defined a methodology to optimize the plot size and the (total) sampling area, allowing for structural attributes with a tolerable error to be estimated. The plot size was assessed by analyzing the semivariogram of a CHM model derived via UAV laser scanning, while the sampling area was based on the calculation of the absolute relative error as a function of allometric relationships. The allometric relationships allowed the structural attributes from trees’ height to be derived. The validation was based on the positioning of a number of trees via total station and GNSS surveys. Since high trees occlude the GNSS signal transmission, a strategy to facilitate the positioning was to fix the solution using the GLONASS constellation alone (showing the highest visibility during the survey), and then using the GPS constellation to increase the position accuracy (up to PDOP~5−10). The tree heights estimated via UAV laser scanning were strongly correlated (r2 = 0.98, RMSE = 2.80 m) with those measured in situ. Assuming a maximum absolute relative error in the estimation of the structural attribute (20% within this work), the proposed methodology allowed the portion of the forest surface (≤60%) to be sampled to be quantified to obtain a low average error in the calculation of the above mentioned structural attributes (≤13

Inperlys – independente personal location system

O desenvolvimento de sistemas de localização pedestre com recurso a técnicas de dead reckoning tem mostrado ser uma área em expansão no mundo académico e não só. Existem algumas soluções criadas, no entanto, nem todas as soluções serão facilmente implementadas no mercado, quer seja pelo hardware caro, ou pelo sistema em si, que é desenvolvido tendo em conta um cenário em particular. INPERLYS é um sistema que visa apresentar uma solução de localização pedestre, independentemente do cenário, utilizando recursos que poderão ser facilmente usados. Trata-se de um sistema que utiliza uma técnica de dead reckonig para dar a localização do utilizador. Em cenários outdoor, um receptor GPS fornece a posição do utilizador, fornecendo uma posição absoluta ao sistema. Quando não é possível utilizar o GPS, recorre-se a um sensor MEMS e a uma bússola para se obter posições relativas à última posição válida do GPS. Para interligar todos os sensores foi utilizado o protocolo de comunicações sem fios ZigBee™. A escolha recaiu neste protocolo devido a factores como os seus baixos consumos e o seu baixo custo. Assim o sistema torna-se de uso fácil e confortável para o utilizador, ao contrário de sistemas similares desenvolvidos, que utilizam cabos para interligarem os diferentes componentes do sistema. O sensor MEMS do tipo acelerómetro tem a função de ler a aceleração horizontal, ao nível do pé. Esta aceleração será usada por um algoritmo de reconhecimento do padrão das acelerações para se detectar os passos dados. Após a detecção do passo, a aceleração máxima registada nesse passo é fornecida ao coordenador, para se obter o deslocamento efectuado. Foram efectuados alguns testes para se perceber a eficiência do INPERLYS. Os testes decorreram num percurso plano, efectuados a uma velocidade normal e com passadas normais. Verificou-se que, neste momento, o desempenho do sistema poderá ser melhorado, quer seja a nível de gestão das comunicações, quer a nível do reconhecimento do padrão da aceleração horizontal, essencial para se detectar os passos. No entanto o sistema é capaz de fornecer a posição através do GPS, quando é possível a sua utilização, e é capaz de fornecer a orientação do movimento.The development of pedestrian localization systems using the dead reckoning technique has been growing not only in academic world, but also in the industrial one. There are some solutions that can be found, however, not all of them are easy be implemented due to factors like expensive hardware, or the system architecture which was developed to work in a particular scenario. INPERLYS is a pedestrian location system intended to give a solution for pedestrian location, regardless of the surrounding environment, using resources that can be handled easily. It implements a dead reckoning technique to achieve the user localization. In outdoor environments, it uses the GPS to give the localization, providing an absolute coordinate to the system. When it is not possible establish communication between GPS receiver and satellites, the system uses a MEMS sensor to estimate the distance traveled and a digital compass for orientation. To connect the sensors and the GPS, it is used the wireless ZigBee™ network protocol. This protocol was chosen because it has low power consumption and low cost. The system uses a wireless network because it is comfortable to use with clothes, unlike many similar systems, that use cables to connect all modules of the system. The MEMS accelerometer sensor has the purpose of measuring the horizontal acceleration, at foot level. This acceleration will be used by an algorithm developed to detect steps by recognizing particular patterns in the measured acceleration. When a step is detected, it will be sent to the network coordinator the maximum measured step acceleration, in order to obtain the distance traveled. The system was evaluated in order to understand his reliability. The test consisted in user walking in a flat path, at constant velocity. As result from the test, I realize that the system performance can be improved. The acceleration messages routing management can be enhanced and there are some steps that are not detected. However, the system is able to give the users position, whenever it´s possible to use GPS and it is also able to provide the user´s movement orientation

Development of GNSS data processing procedures for pedestrian navigation in challenging environments

High sensitivity GPS receivers have expanded the use of the GNSS to areas with difficult reception of the signals from the satellites. Difficult conditions indicate attenuation and reflection of the signals. Receivers achieve higher sensitivity of signal reception by larger number of correlators and longer integration time. The present doctoral dissertation deals with classical methods of observation processing in normal conditions used for observation processing in challenging environments. Some of the methods are more effective in difficult conditions than others. Processing of observation data in dynamic and quick changing conditions requires careful and consistent treatment. Otherwise a gross error in position determination can occur. Multipath has the biggest influence on final results and has a special effect on code observations. Therefore, Doppler observations are also included in the position determination. The custom developed procedure of positioning in challenging environments beside well known and established methods includes additional methods which assure higher accuracy and reliability of the determined position. Processing procedures have been tested in different conditions in static and kinematic cases. The acquired results have been compared to those processed inside the receiver. A so-called conditional DGPS has been developed, which determines the position differentially by using only data from strong signals.

Precise positioning in real-time using GPS-RTK signal for visually impaired people navigation system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 24/9/2010.This thesis presents the research carried out to investigate and achieve highly reliable and accurate navigation system of guidance for visually impaired pedestrians. The main aim with this PhD project has been to identify the limits and insufficiencies in utilising Network Real-Time Kinematic Global Navigation Satellite Systems (NRTK GNSS) and its augmentation techniques within the frame of pedestrian applications in a variety of environments and circumstances. Moreover, the system can be used in many other applications, including unmanned vehicles, military applications, police, etc. NRTK GNSS positioning is considered to be a superior solution in comparison to the conventional standalone Global Positioning System (GPS) technique whose accuracy is highly affected by the distance dependent errors such as satellite orbital and atmospheric biases. Nevertheless, NRTK GNSS positioning is particularly constrained by wireless data link coverage, delays of correction and transmission and completeness, GPS and GLONASS signal availability, etc., which could downgrade the positioning quality of the NRTK results. This research is based on the dual frequency NRTK GNSS (GPS and GLONASS). Additionally, it is incorporated into several positioning and communication methods responsible for data correction while providing the position solutions, in which all identified contextual factors and application requirements are accounted. The positioning model operates through client-server based architecture consisted of a Navigation Service Centre (NSC) and a Mobile Navigation Unit (MNU). Hybrid functional approaches were consisting of several processing procedures allowing the positioning model to operate in position determination modes. NRTK GNSS and augmentation service is used if enough navigation information was available at the MNU using its local positioning device (GPS/GLONASS receiver).The positioning model at MNU was experimentally evaluated and centimetric accuracy was generally attained during both static and kinematic tests in various environments (urban, suburban and rural). This high accuracy was merely affected by some level of unavailability mainly caused by GPS and GLONASS signal blockage. Additionally, the influence of the number of satellites in view, dilution of precision (DOP) and age corrections (AoC) over the accuracy and stability of the NRTK GNSS solution was also investigated during this research and presented in the thesis. This positioning performance has outperformed the existing GPS service. In addition, utilising a simulation evaluation facility the positioning model at MNU performance was quantified with reference to a hybrid positioning service that will be offered by future Galileo Open Service (OS) along with GPS. However, a significant difference in terms of the service availability for the advantage of the hybrid system was experienced in all remaining scenarios and environments more especially the urban areas due to surrounding obstacles and conditions. As an outcome of this research a new and precise positioning model was proposed. The adaptive framework is understood as approaching an integration of the available positioning technology into the context of surrounding wireless communication for a maintainable performance. The positioning model has the capability of delivering indeed accurate, precise and consistent position solutions, and thus is fulfilling the requirements of visually impaired people navigation application, as identified in the adaptive framework

Adaptive, reliable, and accurate positioning model for location-based services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a new strategy in achieving highly reliable and accurate position solutions fulfilling the requirements of Location-Based Services (LBS) pedestrians’ applications. The new strategy is divided into two main parts. The first part integrates the available positioning technology within the surrounding LBS application context by introducing an adaptive LBS framework. The context can be described as a group of factors affecting the application behaviour; this includes environmental states, available resources and user preferences. The proposed adaptive framework consists of several stages, such as defining the contextual factors that have a direct effect on the positioning performance, identifying preliminary positioning performance requirements associated with different LBS application groups, and introducing an intelligent positioning services selection function. The second part of this work involves the design and development of a novel positioning model that is responsible for delivering highly reliable, accurate and precise position solutions to LBS users. This new model is based on the single frequency GPS Standard Positioning Service (SPS). Additionally, it is incorporated within the adaptive LBS framework while providing the position solutions, in which all identified contextual factors and application requirements are accounted. The positioning model operates over a client-server architecture including two main components, described as the Localisation Server (LS) and the Mobile Unit (MU). Hybrid functional approaches were developed at both components consisting of several processing procedures allowing the positioning model to operate in two position determination modes. Stand-alone mode is used if enough navigation information was available at the MU using its local positioning device (GPS/EGNOS receiver). Otherwise, server-based mode is utilised, in which the LS intervenes and starts providing the required position solutions. At the LS, multiple sources of GPS augmentation services were received using the Internet as the sole augmentation data transportation medium. The augmentation data was then processed and integrated for the purpose of guaranteeing the availability of valid and reliable information required for the provision of accurate and precise position solutions. Two main advanced position computation methods were developed at the LS, described as coordinate domain and raw domain. The positioning model was experimentally evaluated. According to the reported results, the LS through the developed position computation methods, was able to provide position samples with an accuracy of less than 2 meters, with high precision at 95% confidence level; this was achieved in urban, rural, and open space (clear satellite view) navigation environments. Additionally, the integrity of the position solutions was guaranteed in such environments during more than 90% of the navigation time, taking into consideration the identified integrity thresholds (Horizontal Alert Limits (HAL)=11 m). This positioning performance has outperformed the existing GPS/EGNOS service which was implemented at the MU in all scenarios and environments. In addition, utilising a simulation evaluation facility the developed positioning model performance was quantified with reference to a hybrid positioning service that will be offered by future Galileo Open Service (OS) along with GPS/EGNOS. Using the statistical t-test, it was concluded that there is no significant difference in terms of the position samples’ accuracy achieved from the developed positioning model and the hybrid system at a particular navigation environment described as rural area. The p-value was 0.08 and the level of significance used was 0.05. However, a significant difference in terms of the service integrity for the advantage of the hybrid system was experienced in all remaining scenarios and environments more especially the urban areas due to surrounding obstacles and conditions

Multimodale Annotation geographischer Daten zur personalisierten Fußgängernavigation

Mobilitätseingeschränkte Fußgänger, wie etwa Rollstuhlfahrer, blinde und sehbehinderte Menschen oder Senioren, stellen besondere Anforderungen an die Berechnung geeigneter Routen. Die kürzeste Route ist nicht immer die am besten geeignete. In dieser Arbeit wird das Verfahren der multimodalen Annotation entwickelt, welches die Erweiterung der geographischen Basisdaten durch die Benutzer selbst erlaubt. Auf Basis der durch das Verfahren gewonnenen Daten werden Konzepte zu personalisierten Routenberechnung auf Grundlage der individuellen Anforderungen der Benutzer entwickelt. Das beschriebene Verfahren wurde erfolgreich mit insgesamt 35 Benutzern evaluiert und bildet somit die Grundlage für weiterführende Arbeiten in diesem Bereich.Mobility impaired pedestrians such as wheelchair users, blind and visually impaired, or elderly people impose specific requirements upon the calculation of appropriate routes. The shortest path might not be the best. Within this thesis, the concept of multimodal annotation is developed. The concept allows for extension of the geographical base data by users. Further concepts are developed allowing for the application of the acquired data for the calculation of personalized routes based on the requirements of the individual user. The concept of multimodal annotation was successfully evaluated incorporating 35 users and may be used as the base for further research in the area

Heading drift mitigation for low-cost inertial pedestrian navigation

The concept of autonomous pedestrian navigation is often adopted for indoor pedestrian navigation. For outdoors, a Global Positioning System (GPS) is often used for navigation by utilizing GPS signals for position computation but indoors, its signals are often unavailable. Therefore, autonomous pedestrian navigation for indoors can be realized with the use of independent sensors, such as low-cost inertial sensors, and these sensors are often known as Inertial Measurement Unit (IMU) where they do not rely on the reception of external information such as GPS signals. Using these sensors, a relative positioning concept from initialized position and attitude is used for navigation. The sensors sense the change in velocity and after integration, it is added to the previous position to obtain the current position. Such low-cost systems, however, are prone to errors that can result in a large position drift. This problem can be minimized by mounting the sensors on the pedestrian’s foot. During walking, the foot is briefly stationary while it is on the ground, sometimes called the zero-velocity period. If a non-zero velocity is then measured by the inertial sensors during this period, it is considered as an error and thus can be corrected. These repeated corrections to the inertial sensor’s velocity measurements can, therefore, be used to control the error growth and minimize the position drift. Nonetheless, it is still inadequate, mainly due to the remaining errors on the inertial sensor’s heading when the velocity corrections are used alone. Apart from the initialization issue, therefore, the heading drift problem still remains in such low-cost systems. In this research, two novel methods are developed and investigated to mitigate the heading drift problem when used with the velocity updates. The first method is termed Cardinal Heading Aided Inertial Navigation (CHAIN), where an algorithm is developed to use building ‘heading’ to aid the heading measurement in the Kalman Filter. The second method is termed the Rotated IMU (RIMU), where the foot-mounted inertial sensor is rotated about a single axis to increase the observability of the sensor’s heading. For the CHAIN, the method proposed has been investigated with real field trials using the low-cost Microstrain 3DM-GX3-25 inertial sensor. It shows a clear improvement in mitigating the heading drift error. It offers significant improvement in navigation accuracy for a long period, allowing autonomous pedestrian navigation for as long as 40 minutes with below 5 meters position error between start and end position. It does not require any extra heading sensors, such as a magnetometer or visual sensors such as a camera nor an extensive position or map database, and thus offers a cost-effective solution. Furthermore, its simplicity makes it feasible for it to be implemented in real-time, as very little computing capability is needed. For the RIMU, the method was tested with Nottingham Geospatial Institute (NGI) inertial data simulation software. Field trials were also undertaken using the same low-cost inertial sensor, mounted on a rotated platform prototype. This method improves the observability of the inertial sensor’s errors, resulting also in a decrease in the heading drift error at the expense of requiring extra components