Tracking height of snowkiting jumps based on MEMS barometer-IMU sensors

Abstract

Motivated by the lack of support for snowkiting in modern sports trackers, this thesis investigates height estimation of snowkiting jumps. Since a jump with a kite does not involve free-fall due to the pull of the kite, traditional jump algorithms cannot be used. By utilizing a consumer grade MEMS barometer-IMU, a sensor fusion is implemented in order to estimate jump trajectories and present the user with performance values such as: jump height, drop and airtime.

Due to the harsh and windy conditions of snowkiting barometric sensor data can be noisy and therefore a feed forward indirect Kalman filter with a RTS smoother is implemented and compared against a direct Kalman filter with a RTS smoother. By mounting the sensor directly on the snowboard or ski an algorithm was developed in order to detect the exact of takeoff and landing.

Testing in the field showed that barometers are highly sensitive to wind and waterproof trackers needs more than one ventilation hole, otherwise an increasing pressure can build up inside the sensor. With an unreliable barometric sensor the feed-forward indirect filter showed good performance on single jump data sets without drifting or being too affected by noisy barometric data. Due to its complementary design it utilizes the accelerometer during sharp transients such as takeoff and landing while trusting the barometer before takeoff and after landing for estimating initial and final altitude of the jump. The proposed solution achieved a jump height RMSE of 0.48m over 33 jumps, with jumps varying from 1.56m to 7.15m.