Matthew B. Rhudy

A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation

A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF) is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI) scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver’s position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV) research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm.

Aircraft model-independent airspeed estimation without pitot tube measurements

This paper presents a novel analytical redundancy approach for pitot tube failure accommodation using nonlinear Kalman filtering. This approach utilizes information from other sensors that are commonly implemented on aircraft in order to obtain an estimate of the airspeed which is independent from the pitot tube(s). This method was demonstrated to be independent of the aircraft model by providing experimental estimation results from two different unmanned aerial vehicle (UAV) research platforms.

Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems

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.

Tracking Steps on Apple Watch at Different Walking Speeds

Key PointsQuestionHow accurate are the step counts obtained from Apple Watch?FindingsIn this validation study, video steps vs. Apple Watch steps (mean ± SD) were 2965 ± 144 vs. 2964 ± 145 steps; P < 0.001. Lin’s concordance correlation coefficient showed a strong correlation (r = 0.96; P < 0.001) between the two measurements. There was a total error of 0.034% (1.07 steps) for the Apple Watch steps when compared with the manual counts obtained from video recordings.MeaningOur study is one of the initial studies to objectively validate the accuracy of the step counts obtained from Apple watch at different walking speeds. Apple Watch tested to be an extremely accurate device for measuring daily step counts for adults.

On-Line Orientation Calibration of Inertial Measurement Unit Pairs using Unmanned Aerial Vehicle Flight Data

Calibration of the orientation of multiple Inertial Measurement Unit (IMU) pairs was performed using flight data collected from six separate flights by the West Virginia University (WVU) ‘Red Phastball’ Unmanned Aerial Vehicle (UAV). Four closely grouped IMUs were located in the front of the UAV for all six flights, and two sets of flight data featured a fifth IMU positioned at the rear of the UAV. This enabled calibration of IMU pairs with negligible distance as well as calibration of spatially separated IMUs, which were modeled assuming rigid body mechanics. Accelerometer and rate gyroscope data were used to produce two linearly independent vectors which were used to define the relative orientation of each IMU pair via an Unscented Kalman Filter (UKF). Using static UAV data recorded before each flight, the gravity vector was used for off-line calibration of the pitch and roll of each IMU with respect to the Earth reference frame, which was used as a truth reference to evaluate the accuracy of the on-line calibration techniques.

A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals

Abstract 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.

A dynamic model-aided sensor fusion approach to aircraft attitude estimation

Attitude is an important consideration for aircraft due to its necessity for control and other purposes such as remote sensing. Sensor fusion techniques are a popular approach to attitude estimation since low-cost and lightweight sensors such as inertial sensors can be utilized. However, an additional source of information that has been mostly overlooked is the control inputs. This information is typically known, and when coupled with an aircraft dynamic model, can predict the aircraft states. This information when fused with other sensor measurements through Kalman filtering techniques offers a reasonable method for using all available information to predict aircraft attitude. This work presents the procedure for implementing this sensor fusion idea with some simulation results from a known aircraft dynamic model.

PS 17-27 BREAKS IN SEDENTARY BEHAVIOR: DURATION VS. FREQUENCY

Objective: Sedentary behaviors increase the risk of cardiovascular disease events and mortality, independently of physical activity levels. However, little is known about patterns of sedentary behavior among University employees. Design and method: Thirty-four (12M, 22F; 48.8 ± 11.3yrs) apparently healthy faculty/staff were recruited from the Pennsylvania State University, Berks. Sedentary time was measured by an ActiGraph GT9X Link accelerometer (counts/min < 100) worn on the wrist for one-week. Each interruption in sedentary time (counts/min ≥100) lasting for 5 continuous minutes was considered a break. Total length of sedentary breaks/week (sum of all the times between sedentary bouts), average length of sedentary breaks/day (total length of sedentary breaks/total sedentary breaks.day−1) and mean caloric expenditure/day were estimated using the Actilife software. Results: Males vs. females (Mean ± SD) for total length of sedentary breaks/week (6913.75 ± 591.16 vs. 6194.73 ± 645.40min; p = 0.003); average length of sedentary breaks/day (811.74 ± 97.91 vs. 703.33 ± 134.22min; p = 0.02) and mean caloric expenditure/day (1682 .71 ± 568.48 vs. 1249.48 ± 508.15kcals; p = 0.29). Average length of sedentary breaks/day was associated with mean caloric expenditure/day (r = 0.37; p = 0.03). There was no statistically significant difference in total sedentary breaks/day (111 vs. 107) between the two groups. Conclusions: Male employees spent ∼13% more time and expended about ∼25% more calories during their breaks compared to their female counterparts. These results suggest that duration of interruptions in sedentary time rather than the frequency of breaks, is associated with increased caloric expenditure. This highlights the importance of interrupting the sedentary pattern by taking longer, active breaks.

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named “Phastball” and a set of quadrotors named “NEO.” Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors.