Tracking height of snowkiting jumps based on MEMS barometer-IMU sensors
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Motivated by the lack of support for snowkiting in modern sports trackers, this thesisinvestigates height estimation of snowkiting jumps. Since a jump with a kite does notinvolve 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 inorder 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 noisyand therefore a feed forward indirect Kalman filter with a RTS smoother is implementedand compared against a direct Kalman filter with a RTS smoother. By mounting the sensordirectly on the snowboard or ski an algorithm was developed in order to detect the exactof takeoff and landing. Testing in the field showed that barometers are highly sensitive to wind and waterprooftrackers needs more than one ventilation hole, otherwise an increasing pressure can buildup inside the sensor. With an unreliable barometric sensor the feed-forward indirect filtershowed good performance on single jump data sets without drifting or being too affectedby noisy barometric data. Due to its complementary design it utilizes the accelerometerduring sharp transients such as takeoff and landing while trusting the barometer beforetakeoff and after landing for estimating initial and final altitude of the jump. The proposedsolution achieved a jump height RMSE of 0.48m over 33 jumps, with jumps varying from1.56m to 7.15m.