Implementation and Performance of a GPS/INS Tightly Coupled Assisted PLL Architecture Using MEMS Inertial Sensors

The use of global navigation satellite system receivers for navigation still presents many challenges in urban canyon and indoor environments, where satellite availability is typically reduced and received signals are attenuated. To improve the navigation performance in such environments, several enhancement methods can be implemented. For instance, external aid provided through coupling with other sensors has proven to contribute substantially to enhancing navigation performance and robustness. Within this context, coupling a very simple GPS receiver with an Inertial Navigation System (INS) based on low-cost micro-electro-mechanical systems (MEMS) inertial sensors is considered in this paper. In particular, we propose a GPS/INS Tightly Coupled Assisted PLL (TCAPLL) architecture, and present most of the associated challenges that need to be addressed when dealing with very-low-performance MEMS inertial sensors. In addition, we propose a data monitoring system in charge of checking the quality of the measurement flow in the architecture. The implementation of the TCAPLL is discussed in detail, and its performance under different scenarios is assessed. Finally, the architecture is evaluated through a test campaign using a vehicle that is driven in urban environments, with the purpose of highlighting the pros and cons of combining MEMS inertial sensors with GPS over GPS alone.

Advanced Algorithmic and Architecture Designs for Future Satellite Navigation Receivers

Youssef Tawk

The use of global navigation satellite system (GNSS) receivers for navigation still presents many challenges, in particular in urban canyon and indoor environments where satellite availability is reduced and received signals are usually much atten- uated. In addition, the reception of additional signal replicas due to reflections on the surrounding environment, i.e. multipath, introduces biases in the pseudorange measurements, which in turn lead to extra positioning errors. The navigation per- formance of a GNSS receiver depends greatly on the behavior of the phase lock loop (PLL) and the delay lock loop (DLL). To maintain the robustness of these loops in such conditions, several enhancement methods can be implemented to improve upon standard stand-alone mass market receivers. For instance, well-known techniques include the use of multi-constellations to improve the availability of visible satellites, take advantage of the potential multipath mitigation of the new GNSS signals, and an increase of the integration time combined with a decrease of the PLL and DLL filters bandwidths to improve sensitivity. Moreover, external aiding in the form of time, Doppler, position, or almanac that can be provided through coupling with other sensors can also contribute substantially in improving navigation performance in challenging environments. The aim of this dissertation is to address the challenges of satellite based naviga- tion in demanding environments in order to improve the navigation performance of the future GNSS receivers. Within this context, two research directions are adopted in this thesis. The first is to explore the performance and advantages of the upcoming Galileo signals and in particular the E5 Alternate Binary Offset Carrier AltBOC(15,10), and the second is to investigate the potential of low-cost micro-electro-mechanical systems (MEMS) based inertial sensors to complement GNSS receivers. In the first research direction, we present investigations of the processing of Galileo E5ab in a full band or of only one of its components, i.e. either E5a or E5b. More specifically, a new acquisition algorithm is proposed for wiping off the secondary code and thereby increase the coherent integration time while requiring a reasonable implementation complexity as compared to other architectures. Moreover, an archi- tecture for tracking the E5 pilot channel as an AltBOC(15,10) or BPSK(10) modulation is introduced, and the performance of well-known discriminator types is analyzed using analytical derivations and simulations of linearity and stability regions, thermal noise tracking errors, multipath error envelopes and tracking thresholds. Different parameters, such as the front-end filter bandwidth, the early/late chip spacing, un- normalized and normalized discriminators, are taken into consideration. The results we obtain are used to illustrate the main advantages and drawbacks of using the E5 signal in demanding environments as well as to help defining the main tracking loop parameters for an enhanced performance. In the second research direction, we consider the coupling of a global positioning system (GPS) receiver with an inertial navigation system (INS) based on MEMS sen- sors. In the past, one of the main constraints holding back the proliferation of such hybrid systems was the price of the inertial sensors, but with the widespread dissemi- nation of MEMS-based sensors this is no longer the case. Therefore, a GPS/INS Tightly Coupled Assisted PLL (TCAPLL) architecture is proposed in this dissertation, and most of the associated challenges that need to be addressed when dealing with very- low-performance MEMS inertial sensors are presented. The architecture includes a data monitoring system responsible for checking the quality of the measurement flow to maintain robust tracking and accurate navigation. The implementation of the TCAPLL is discussed in detail, and its performance under different scenarios is assessed. Finally the proposed architecture is evaluated through a test campaign using a vehicle that is driven in urban environments, with the purpose of highlighting the pros and cons of combining MEMS inertial sensors with GPS over GPS alone.

EPFL2013

GPS/INS integration for pedestrian navigation

This research has been sponsored by the Centre Suisse d'Electronique et de Microtechnique (CSEM) in Neuchâtel, Switzerland. It introduces a system and the algorithms for Pedestrian Navigation using a combination of sensors. The main objective is to localise a pedestrian anywhere and at any moment. The equipments utilised to fulfill this requirement are a Global Navigation Satellite System (GNSS) receiver and inertial sensors, which are attached to the person and as such need to be portable. An overview of Pedestrian Navigation constitutes the first part of the document. This new domain is examined from four different views: applications, tools (or sensors), architecture of the system and finally environment in which the pedestrian is travelling. As part of this process, the "state of the art" situation is presented. The approach followed in order to aid pedestrian to navigate is based on the Dead Reckoning technique coupled with GNSS. Consequently, the resolution of the travelled "distance" is separated from the resolution of the orientation of the walk. For the computation of the distance, a new technique based upon accelerometers and GNSS has been developed and demonstrated. The accelerometers are not used as a classical pedometer (counter of the number of steps) and the technique is not based on the double integration to obtain successively speed and distance. Instead, signal processing allows, considering individual parameters, the walking speed to be obtained directly from the signal of the accelerometers. This process, as well as the manner to determine the individual parameters, is presented in detail. The development of the algorithms is based on research performed in both the navigation and the medical domains (mainly in physiology). The computation of the orientation is more classical. It is based on the measurements made by a gyroscope and a GNSS receiver. The particularity of this study is the use of a single gyroscope to determine the orientation of the walk instead of three for the classical technique of inertial navigation. The influence of body movement on the gyroscope output has been deeply examined to determine the most appropriate way to process the signal of the gyroscope. The feasibility of the use of a single gyro, in the context of pedestrian navigation, is demonstrated. The potential added value for introducing a magnetic compass in the system is also evaluated. Finally a centralised Kalman filter has been designed and tested to merge all the sensors outputs, to combine the distance and the orientation, to integrate the Dead Reckoning solution and the GNSS solutions and to estimate all the parameters in a close to real-time process. The efficiency of this filter is demonstrated through different tests.

EPFL2003

Bayesian Approach for Indoor Pedestrian Localisation

Michel Bierlaire, Bertrand Merminod

The principal concept of navigation is to start from a known (initial) position and to ensure a continued and reliable localisation of the user during his/her movement. The initial position of the trajectory is usually obtained via GPS or defined by the user. Consider a pedestrian navigation system which contains a GPS receiver and a set of inertial sensors, connected with a map database. In the urban environment and indoors the localisation depends entirely on the measurements from the inertial sensors. The trajectory is defined in a local coordinate system and with an arbitrary orientation. The problem to solve is to determine the users location using the map database and inertial measurements of the navigation system. The idea behind our approach is to find the location and orientation of the trajectory and thus the users location. The proposed solution associates the users trajectory with the map database applying statistical methods in combination with map-matching. Similar geometric forms must be identified in both the trajectory and the link-node model. The trajectory, defined by a set of consecutive points, is transformed to a set of lines thanks to a dedicated motion model. In this research we propose a solution based on statistical methods where the history of the route and actual measurements are treated at the same time. The determination of the absolute position is entirely represented by its probability density function (PDF) in the frame of Bayesian inference. Following this approach the posterior estimation of the users location can be calculated using prior information and actual measurements. Because of the non-linear nature of the estimation problem, non-linear filtering techniques like particle filters (Sequential Monte Carlo methods) are applied.

2006

Advanced Algorithmic and Architecture Designs for Future Satellite Navigation Receivers

Youssef Tawk

EPFL2013

GPS/INS integration for pedestrian navigation

EPFL2003