Construction of dynamically-dependent stochastic error models, 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS)

Construction of dynamically-dependent stochastic error models

Philipp Clausen, Stéphane Guerrier, Jan Skaloud

Stochastic behavior of an instrument is often analyzed by constructing the Allan (or wavelet) variance signatures from an error signal. For inertial sensors, such a signature is conveniently obtained by recording data at rest. The analysis of this signal will result in noise-parameters adequate to such situation. Nonetheless, the value of the noise parameters may change under dynamics or other kind of external influences like for instance the temperature. In this research we study first the influence of the rotational dynamics on the signal of MEMS gyroscopes and then we show how to link this property to the noise-parameter estimation in a rigorous way by a modified version of the Generalized Method of Wavelet Moments (GMWM) estimator. The results of such analysis can then for instance be used in a Kalman filter, where the noise parameters are adapted according to such predetermined functional relationship between sensor noise and the encountered dynamics of the platform/sensor.

2018

Construction of Dynamically-Dependent Stochastic Error Models

Philipp Clausen, Stéphane Guerrier, Jan Skaloud

Stochastic behavior of an instrument is often analyzed by constructing the Allan (or wavelet) variance signatures from an error signal. For inertial sensors, such a signature is conveniently obtained by recording data at rest. The analysis of this signal will result in noise-parameters adequate to such situation. Nonetheless, the value of the noise parameters may change under dynamics or other kind of external influences like for instance the temperature. In this research we study first the influence of the rotational dynamics on the signal of MEMS gyroscopes and then we show how to link this property to the noise-parameter estimation in a rigorous way by a modified version of the Generalized Method of Wavelet Moments (GMWM) estimator. The results of such analysis can then for instance be used in a Kalman filter, where the noise parameters are adapted according to such predetermined functional relationship between sensor noise and the encountered dynamics of the platform/sensor.

IEEE2018

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