Vol 9, No 1 (2018)

Inertial sensors are powerful motion measurement devices. They are well-known in vehicle guidance and enable a detailed capture of position, attitude, velocity, and acceleration. Due to modern technology, navigation systems based on these sensors became increasingly small, light, and inexpensive. So, they suggest themselves for motion analysis in sports as an arising application area. Considering the last decades, this paper outlines and discusses the introduction and typical usage of inertial and integrated navigation systems in sports and biomechanics respectively.

The experience in the development of a gyrocompass based on a laser gyroscope rotating about its horizontal axis of sensitivity is discussed. Levels of errors caused by various sources are estimated. The gyrocompass mockup is described, and the experimental results are presented.

The paper presents the information on specific design, accuracy characteristics, and results of testing of a prototype fiber-optic gyro of accuracy grade 0.01°/h, with sensing element spool diameter of 150 mm, designed by Concern RCSI Elektropribor, JSC. The device is compared to a number of sensors of similar accuracy grade, designed by various Russian and international companies.

Testing of an inertial navigation system (INS) aimed to verify its compliance with the requirements for the accuracy of its heading channel by comparing it with a reference INS with known accuracy parameters is discussed. It is shown that the main conditions for successful testing are the following two factors: the proportion (ratio) of the errors of the heading channel being tested and those of the reference channel, and the estimation accuracy of the reference channel errors. Formulas are obtained for the case of a zero systematic error of the reference heading channel, which provide reliable estimation of the comparison results.

This paper aims at investigating and analyzing the behavior of Micro-Electromechanical Systems (MEMS) inertial sensors stochastic errors in both static and varying dynamic conditions using two MEMSbased Inertial Measurement Units (IMUs) of two different smartphones. The corresponding stochastic error processes were estimated using two different methods, the Allan Variance (AV) and the Generalized Method of Wavelets Moments (GMWM). The developed model parameters related to laboratory dynamic environment are compared to those obtained under static conditions. A contamination test was applied to all data sets to distinguish between clean and corrupted ones using a Confidence Interval (CI) investigation approach. A detailed analysis is presented to define the link between the error model parameters and the augmented dynamics of the tested smartphone platform. The paper proposes a new dynamically dependent integrated navigation algorithm which is capable of switching between different stochastic error parameters values according to the platform dynamics to eliminate dynamics-dependent effects. Finally, the performance of different stochastic models based on AV and GMWM were analyzed using simulated Inertial Navigation System (INS)/Global Positioning System (GPS) data with induced GPS signal outages through the new proposed dynamically dependent algorithm. The results showed that the obtained position accuracy is improved when using dynamic-dependent stochastic error models, through the adaptive integrated algorithm, instead of the commonly used static one, through the non-adaptive integrated one. The results also show that the stochastic error models from GMWM-based model structure offer better performance than those estimated from the AV-based model.