-In this article, we investigate two different algorithms for the integration of GPS with redundant MEMS-IMUs. Firstly, the inertial measurements are combined in the observation space to generate a synthetic set of data which is then integrated with GPS by the standard algorithms. In the second approach, the method of strapdown navigation needs to be adapted in order to account for the redundant measurements. Both methods are evaluated in experiments where redundant MEMSIMUs are fixed in different geometries: orthogonallyredundant and skew-redundant IMUs. For the latter configuration, the performance improvement using a synthetic IMU is shown to be 30% on the average. The extended mechanization approach provides slightly better results (about 45% improvement) as the systematic errors of the individual sensors are considered separately rather than their fusion when forming compound measurements. The maximum errors are shown to be reduced even by a factor of 2.

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.

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 of 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-IMUs. Throughout the paper, our findings are supported by racing experiments with references provided in both, the measurement and the navigation domains.

This research presents methods for detecting and isolating faults in multiple MEMS-IMU configurations. First, geometric configurations with n sensor triads are investigated. It is proofed that the relative orientation between sensor triads is irrelevant to system optimality in the absence of failures. Then, the impact of sensor failure or decreased performance is investigated. Three FDI approaches (i.e. the parity space method, Mahalanobis distance method and its direct robustification) are reviewed theoretically and in the context of experiments using reference signals. It is shown that in the presence of multiple outliers the best performing detection algorithm is the robust version of the Mahalanobis distance.

Inertially aided satellite positioning can bring its benefits to all disciplines in which detailed knowledge of the trajectory is a prerequisite for improving performance. In motorcycling for instance, the determination of slips of tires requires the determination of the precise trajectory and the orientation of the motorcycle's chassis. The correct exploitation of torque or force sensors as well as studies of the vibratory behavior of pneumatics necessitate the knowledge of the orientation of the sensors. Accurate position and orientation can be obtained by integrating inertial measurement units (IMU) with GPS (Global Positioning System). Unfortunately, the traditional, bulky and expensive high-quality GPS/IMU instrumentation is restricted to few disciplines with higher accuracy demands, while the ergonomic constraints of some sports (e.g. ski racing, motorcycling) urge to use devices based on mono-frequency differential GPS and Micro-Electro-Mechanical System (MEMS) inertial technology. Due to their small size, low cost and power consumption, MEMS sensors are suitable for trajectory analysis in sports where ergonomic aspects play an important role. In this article, an experimental low-cost differential GPS/MEMS-IMU system is applied in motorcycling. The system provides an absolute positional accuracy better than 0.5m, velocity estimates accurate to 0.2m/s and an orientation accuracy of 1-2°.