This paper presents a novel wind estimation approach, which is compared with existing ideas utilizing different combinations of common aircraft sensors to estimate the wind velocity in real time at the location of an aircraft. These different techniques were evaluated using simulation data as well as two experimental unmanned aircraft flight tests using validation data from a ground weather station. Significant performance advantage was shown of the new filtering technique over the existing approaches.

Accelerometer cut points are an important consideration for distinguishing the intensity of activity into categories such as moderate and vigorous. It is well-established in the literature that these cut points depend on a variety of factors, including age group, device, and wear location. The Actigraph GT9X is a newer model accelerometer that is used for physical activity research, but existing cut points for this device are limited since it is a newer device. Furthermore, there is not existing data on cut points for the GT9X at the ankle or foot locations, which offers some potential benefit for activities that do not involve arm and/or core motion. A total of N = 44 adults completed a four-stage treadmill protocol while wearing Actigraph GT9X sensors at four different locations: foot, ankle, wrist, and hip. Metabolic Equivalent of Task (MET) levels assessed by indirect calorimetry along with Receiver Operating Characteristic (ROC) curves were used to establish cut points for moderate and vigorous intensity for each wear location of the GT9X. Area under the ROC curves indicated high discrimination accuracy for each case.

This paper proposes a novel dynamic model-aided navigation filter to estimate the safety-critical states of a high-altitude long-endurance (HALE) UAV without measurement of angle of attack (AOA) and sideslip angle (SSA). The major contribution of the proposed algorithm is that the synthetic AOA and SSA measurements are newly formulated for analytical redundancy. In the proposed filter, aerodynamic coefficients and control signals are utilized along with inertial measurement unit (IMU), Global Positioning System (GPS), and pitot tube measurements to estimate the navigation states as well as the steady and turbulent effects of 3D wind using random walk (RW) and Dryden wind models, respectively. Flight test results of a HALE UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, AOA, SSA, attitude, angular rates, and 3D wind states, demonstrating its effectiveness for analytical redundancy.

This paper proposes a novel dynamic model-aided navigation filter to estimate the safety-critical states of an aircraft including the effect of wind. Aerodynamic coefficients and control signals are used to predict the angular rates. Experimental flight results of a high-altitude long-endurance (HALE) UAV demonstrated improvement in attitude estimation compared to a model-based navigation algorithm that does not consider wind, as well as accurate attitude estimation without using gyroscope signals, demonstrating its effectiveness for analytical redundancy.

The goal of this work is to compare the differences between various step counting algorithms using both accelerometer and gyroscope measurements from wrist and ankle-mounted sensors. Participants completed four different conditions on a treadmill while wearing an accelerometer and gyroscope on the wrist and the ankle. Three different step counting techniques were applied to the data from each sensor type and mounting location. It was determined that using gyroscope measurements allowed for better performance than the typically used accelerometers, and that ankle-mounted sensors provided better performance than those mounted on the wrist.