SARHA – Development of a Sensor-Augmented GPS/EGNOS/Galileo Receiver for Urban and Indoor Environments

The main objective of the project ‘SARHA - Sensor-Augmented EGNOS/Galileo Receiver for Handheld Applications in Urban and Indoor Environments’ is the development of a modern satellite navigation receiver with autonomous sensor augmentation. Additionally, the hybrid navigation system will be enhanced by a transponder capable of receiving absolute position updates from transmitters installed inside buildings. The transponder will allow the SARHA system to significantly increase reliability and accuracy of the positioning solutions indoors. The hybrid navigation software is split up into two parts. The first one will be implemented directly on the GPS receiver, whereas the second part will run on a microcontroller. Thus, a small, low-performance microcontroller can be used, representing the first step towards the reduction of size, weight and power consumption of the mobile system. This paper provides an overview on personal mobility and typical applications related to the system, describes the system architecture and the hybrid navigation software in detail. Furthermore, emphasis is laid on a comparison of different step detection algorithms, showing their advantages and disadvantages. Based on the Galileo signal definition, additional analysis set up to explore the signal characteristics in comparison to the GPS signals, are provided. Improvements due to the Galileo signal availability in urban and indoor environments are assessed and will later ensure seamless integration of enhanced technologies into the continuous developments.

Sensor-Augmented EGNOS/Galileo Receiver for Handheld Applications in Urban and Indoor Environments - SARHA

Phillip Tomé, Okan Yalak

In pedestrian user environments, like dense urban canyons or indoors, the GNSS positioning performance regarding accuracy and availability reaches well-known technological limits. These limitations can be overcome by adding additional sources of information to the system. The main objective of the project ‘SARHA - Sensor-Augmented EGNOS/Galileo Receiver for Handheld Applications in Urban and Indoor Environments’ is the combination of a modern satellite navigation receiver with augmentation and autonomous sensors. Additionally, the hybrid navigation system will be enhanced by a transponder receiver, which communicates with transmitters installed inside buildings, to provide absolute position updates. The transponder will allow the SARHA system to significantly increase reliability and accuracy of the positioning solutions indoors. The hybrid navigation software is split up into two parts. The first one will be implemented directly on the GPS receiver, whereas the second part will run on a microcontroller. In this way a small, low-performance microcontroller can be used, what represents the first step to reduce dimension, weight and energy consumption of the mobile system. The project aims at meeting the requirements of fire fighters, rescue services, police, special task forces, solitary, and lone workers for handheld applications acting in environments with unfavourable satellite signal conditions, using hybrid navigation system architecture. Based on the Galileo signal definitions, analyses are set up to explore the signal characteristics in comparison to the GPS signals. Improvements due to Galileo signal availability in urban and indoor environments are assessed and will later ensure seamless integration of enhanced technologies into continuous developments.

2006

Hybridization of MEMS and Assisted GPS for Pedestrian Navigation

Phillip Tomé, Okan Yalak

Exploration of hybrid inertial/GPS systems to provide continuous positioning through varying environments represents a growing field of interest to researchers. In particular, first responders and public safety professional would like to see robust systems that can operate indoors. This article describes work under way to develop a pedestrian navigation system based on inertial sensors and an assisted GNSS receiver. Special attention is paid to efforts being made in "gait analysis" to fine-tune the behavior of the inertial sensors as well as the stochastic models used in the system.

2007

Algorithms for map-aided autonomous indoor pedestrian positioning and navigation

The personal positioning and navigation became a very challenging topic in our dynamic time. The urban canyons and particularly indoors represent the most difficult areas for personal navigation problematic. Problems like disturbed satellite signals make the positioning impossible indoors. Recently developed systems for indoor positioning do not assure the necessary positioning accuracy or are very expensive. Our concept stands for a fully autonomous positioning and navigation process. That is, a method that does not rely on the reception of external information, like satellite or terrestrial signals. Therefore, this research is based on the use of inertial measurements of the human walk and the map database which contains the graphic representation of the elements of the building, created by applying the link-node model. Using this reduced set of information the task is to develop methodology, based on the interaction of the data from both sources, to assure reliable positioning and navigation process. This research is divided in three parts. The first part consists in the development of a methodology for initial localization of the person indoors. The problem to solve is to localize the person in the building. Consider a person equipped with a system which contains set of inertial sensors and map database of the building. Speed, turn rate and barometric altitude are measured and time-stamped on each step of the person. A pre-processing phase uses these raw measurements in order to construct a polyline, thus representing user's trajectory. In the localization approach central place takes the association of the user's trajectory with the graph representation of the building, process known as map-matching. The solution is based on statistical method where the determination of the user's position is entirely represented by its probability density function (PDF) in the frame of Bayesian inference. Initial localization determines the edge of the graph occupied by the person. The second part aims at continuous localization, where user's position is estimated on every step. Besides the application of the classical map-matching techniques, two new methods are developed. Both rely on the similarity of the geometry of the trajectory and the elements of the graph. The first is based on the Bayesian inference, where the estimation is computed considering the walked distance and azimuth. The second method represents a new application of the Fréchet distance as degree of similarity between two polylines. The third part is pointed at the pedestrian guidance. Once the user's position is known it is easy to compute the path to his destination and to give him directions. The problem is to assure continuance of the process of navigation in the case when the person has lost his path. In that case the solution consists in either giving instructions to the user to go back on the path or computation of a new path from the actual position of the user to his destination. Based on that methodology, algorithms for initial localization, continuous localization, and guidance were created. Numerous tests with the participation of several persons have been provided in order to validate the algorithms and to show their performance, robustness and limits.

EPFL2007

Algorithms for map-aided autonomous indoor pedestrian positioning and navigation

EPFL2007