Trajectory determination and analysis in sports by satellite and inertial navigation

This research presents methods for performance analysis in sports through the integration of Global Positioning System (GPS) measurements with Inertial Navigation System (INS). The described approach focuses on strapdown inertial navigation using Micro-Electro-Mechanical System (MEMS) Inertial Measurement Units (IMU). A simple inertial error model is proposed and its relevance is proven by comparison to reference data. The concept is then extended to a setup employing several MEMS-IMUs in parallel. The performance of the system is validated with experiments in skiing and motorcycling. The position accuracy achieved with the integrated system varies from decimeter level with dual-frequency differential GPS (DGPS) to 0.7 m for low-cost, single-frequency DGPS. Unlike the position, the velocity accuracy (0.2 m/s) and orientation accuracy (1 – 2 deg) are almost insensitive to the choice of the receiver hardware. The orientation performance, however, is improved by 30 – 50% when integrating four MEMS-IMUs in skew-redundant configuration. Later part of this research introduces a methodology for trajectory comparison. It is shown that trajectories based on dual-frequency GPS positions can be directly modeled and compared using cubic spline smoothing, while those derived from single-frequency DGPS require additional filtering and matching

Optimization of two GPS/MEMS-IMU integration strategies with application to sports

The application of low-cost L1 GPS receivers integrated with micro-electro-mechanical system (MEMS) inertial measurement units (IMU) allows the continuous observation of position, velocity and orientation which opens new possibilities for comparison orf athletes' performance throughout a racecourse. In this paper, we compare loosely and closely coupled integration strategies under realistic racing scenarios when GPS is partially or completely masked. The study reveals that both integration approaches have a similar performance when the satellite constellation is completed or the outages are short. However, for less than four satellites, the closely coupled strategy clearly outperforms the loosely coupled approach. The second part of the paper is devoted to the important problem of system initialization, because the conventional GPS/IMU alignment methods are no longer applicable when using MEMS-IMU. We introduce a modified coarse alignment method and a quaternion estimation method for the computation of the initial orientation. Simulations and practical experiments reveal that both methods are numerically stable for any initial orientation of the sensors with the error characteristics of MEMS-IMU's. Throughout the paper, our findings are supported by racing experiments with references provided in both, the measurement and the navigation domain

The use of wavelet transform for an automated initialization in GPS/MEMS-IMU integration

The main objective of this research is in automating the initialization phase of the MEMS-IMZ/GPS data integration. The motivation for this study is the special case where the before mentioned sensors are worn on a body (e.g. of an athlete) and where the usually used static assumptions (i.e. zero velocity) are difficult to satisfy. Nevertheless, the proposed methodology is also applied on terrestrial vehicles with the aims of reducing the user interactions in the reconstruction of the trajectory from the recorded data. The proposed identification of dynamic versus (quasi) static periods is based on wavelet decomposition of the inertial measurements. After presenting the bases of the process using the Continuous Wavelet Transform (CWT), the automated software's architecture is presented together with experiences carried in different dynamic environments. The trajectories calculated with the automated initialization are compared to those benefiting from the manual selection of the initialization periods based on experience and external knowledge of the underlying motion. As the differences between both approaches are negligible the new method is validated

Noise reduction and estimation in multiple micro-electro-mechanical inertial systems

This research studies the reduction and the estimation of the noise level within a redundant configuration of low-cost (MEMS-type) inertial measurement units (IMUs). Firstly, independent observations between units and sensors are assumed and the theoretical decrease in the system noise level is analyzed in an experiment with four MEMS-IMU triads. Then, more complex scenarios are presented in which the noise level can vary in time and for each sensor. A statistical method employed for studying the volatility of financial markets (GARCH) is adapted and tested for the usage with inertial data. This paper demonstrates experimentally and through simulations the benefit of direct noise estimation in redundant IMU setups