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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.
Wonkeun Youn; Hyoungsik Choi; Am Cho; Sungyug Kim; Matthew B. Rhudy. Aerodynamic Model-Aided Estimation of Attitude, 3-D Wind, Airspeed, AOA, and SSA for High-Altitude Long-Endurance UAV. IEEE Transactions on Aerospace and Electronic Systems 2020, 56, 4300 -4314.
AMA StyleWonkeun Youn, Hyoungsik Choi, Am Cho, Sungyug Kim, Matthew B. Rhudy. Aerodynamic Model-Aided Estimation of Attitude, 3-D Wind, Airspeed, AOA, and SSA for High-Altitude Long-Endurance UAV. IEEE Transactions on Aerospace and Electronic Systems. 2020; 56 (6):4300-4314.
Chicago/Turabian StyleWonkeun Youn; Hyoungsik Choi; Am Cho; Sungyug Kim; Matthew B. Rhudy. 2020. "Aerodynamic Model-Aided Estimation of Attitude, 3-D Wind, Airspeed, AOA, and SSA for High-Altitude Long-Endurance UAV." IEEE Transactions on Aerospace and Electronic Systems 56, no. 6: 4300-4314.
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.
Wonkeun Youn; Hyoung Sik Choi; Hyeok Ryu; Sungyug Kim; Matthew B. Rhudy. Model-Aided State Estimation of HALE UAV With Synthetic AOA/SSA for Analytical Redundancy. IEEE Sensors Journal 2020, 20, 7929 -7940.
AMA StyleWonkeun Youn, Hyoung Sik Choi, Hyeok Ryu, Sungyug Kim, Matthew B. Rhudy. Model-Aided State Estimation of HALE UAV With Synthetic AOA/SSA for Analytical Redundancy. IEEE Sensors Journal. 2020; 20 (14):7929-7940.
Chicago/Turabian StyleWonkeun Youn; Hyoung Sik Choi; Hyeok Ryu; Sungyug Kim; Matthew B. Rhudy. 2020. "Model-Aided State Estimation of HALE UAV With Synthetic AOA/SSA for Analytical Redundancy." IEEE Sensors Journal 20, no. 14: 7929-7940.
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.
Matthew B. Rhudy; Scott B. Dreisbach; Matthew D. Moran; Marissa J. Ruggiero; Praveen Veerabhadrappa. Cut points of the Actigraph GT9X for moderate and vigorous intensity physical activity at four different wear locations. Journal of Sports Sciences 2019, 38, 503 -510.
AMA StyleMatthew B. Rhudy, Scott B. Dreisbach, Matthew D. Moran, Marissa J. Ruggiero, Praveen Veerabhadrappa. Cut points of the Actigraph GT9X for moderate and vigorous intensity physical activity at four different wear locations. Journal of Sports Sciences. 2019; 38 (5):503-510.
Chicago/Turabian StyleMatthew B. Rhudy; Scott B. Dreisbach; Matthew D. Moran; Marissa J. Ruggiero; Praveen Veerabhadrappa. 2019. "Cut points of the Actigraph GT9X for moderate and vigorous intensity physical activity at four different wear locations." Journal of Sports Sciences 38, no. 5: 503-510.
Wonkeun Youn; Matthew B. Rhudy; Am Cho; Hyun Myung. Fuzzy Adaptive Attitude Estimation for a Fixed-Wing UAV With a Virtual SSA Sensor During a GPS Outage. IEEE Sensors Journal 2019, 20, 1456 -1472.
AMA StyleWonkeun Youn, Matthew B. Rhudy, Am Cho, Hyun Myung. Fuzzy Adaptive Attitude Estimation for a Fixed-Wing UAV With a Virtual SSA Sensor During a GPS Outage. IEEE Sensors Journal. 2019; 20 (3):1456-1472.
Chicago/Turabian StyleWonkeun Youn; Matthew B. Rhudy; Am Cho; Hyun Myung. 2019. "Fuzzy Adaptive Attitude Estimation for a Fixed-Wing UAV With a Virtual SSA Sensor During a GPS Outage." IEEE Sensors Journal 20, no. 3: 1456-1472.
With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are taking. For rehabilitation and athletic training, this difference is important to the prescribed regiment. Fourteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) for 90 s. An inertial measurement unit (IMU) with accelerometer and gyroscope was affixed to their left ankle. Collected acceleration and angular velocity data were partitioned into individual time-normalised strides. These data were used as features in the artificial neural network (ANN) that classified the type of stride. Several ANN models were tested: using only acceleration, only angular velocity and both. Using primarily acceleration data in the trained ANN yielded the best results (>94% correct stride-type identification) after cross-validation. The ANN models were able to accurately classify the gait type of each stride using a single wearable IMU. The accuracy of the method should improve further as more data is added to the ANN training.
Joseph M. Mahoney; Matthew B. Rhudy. Methodology and validation for identifying gait type using machine learning on IMU data. Journal of Medical Engineering & Technology 2019, 43, 25 -32.
AMA StyleJoseph M. Mahoney, Matthew B. Rhudy. Methodology and validation for identifying gait type using machine learning on IMU data. Journal of Medical Engineering & Technology. 2019; 43 (1):25-32.
Chicago/Turabian StyleJoseph M. Mahoney; Matthew B. Rhudy. 2019. "Methodology and validation for identifying gait type using machine learning on IMU data." Journal of Medical Engineering & Technology 43, no. 1: 25-32.
Background: Reliable step counting is a critical part of locomotion research. Current counting methods can be inaccurate, time consuming, expensive or encumbering to the subject. Here, we present a camera-based optical method for automatically counting steps. Methods: Fifteen healthy adults walked, jogged and ran on a treadmill at three different constant speeds (1.21, 2.01, 2.68 m/s) and once at varying speed (1.21–2.68 m/s) for 90 s. Subjects had visual marker affixed to their left foot while walking. Video was recorded synchronously at low- and high-resolution during trials. The step count found manually from the video was compared to an automated video analysis system using the two configurations of the optical system. Results: Bland–Altman plots, Intra-class correlation coefficients (ICC) and relative error comparison were used for quantitative assessment of device reliability. Reliability of optical method was high (ICC ≥0.98). Conclusions: The method produces accurate step count results for the range of speeds tested. They use customisable open-source software and off-the-shelf hardware. The method has a low cost of implementation compared to many consumer products and grants researchers access to the raw sensor data.
Joseph M. Mahoney; Zackery E. Scalyer; Matthew B. Rhudy. Design and validation of a simple automated optical step counting method for treadmill walking. Journal of Medical Engineering & Technology 2018, 42, 468 -474.
AMA StyleJoseph M. Mahoney, Zackery E. Scalyer, Matthew B. Rhudy. Design and validation of a simple automated optical step counting method for treadmill walking. Journal of Medical Engineering & Technology. 2018; 42 (6):468-474.
Chicago/Turabian StyleJoseph M. Mahoney; Zackery E. Scalyer; Matthew B. Rhudy. 2018. "Design and validation of a simple automated optical step counting method for treadmill walking." Journal of Medical Engineering & Technology 42, no. 6: 468-474.
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.
Matthew B. Rhudy; Joseph M. Mahoney. A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals. Journal of Medical Engineering & Technology 2018, 42, 236 -243.
AMA StyleMatthew B. Rhudy, Joseph M. Mahoney. A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals. Journal of Medical Engineering & Technology. 2018; 42 (3):236-243.
Chicago/Turabian StyleMatthew B. Rhudy; Joseph M. Mahoney. 2018. "A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals." Journal of Medical Engineering & Technology 42, no. 3: 236-243.
Matthew B. Rhudy; Jason N. Gross; Yu Gu. Determination of Stochastic Wind Speed Model Parameters Using Allan Deviation Approach. AIAA Modeling and Simulation Technologies Conference 2017, 1 .
AMA StyleMatthew B. Rhudy, Jason N. Gross, Yu Gu. Determination of Stochastic Wind Speed Model Parameters Using Allan Deviation Approach. AIAA Modeling and Simulation Technologies Conference. 2017; ():1.
Chicago/Turabian StyleMatthew B. Rhudy; Jason N. Gross; Yu Gu. 2017. "Determination of Stochastic Wind Speed Model Parameters Using Allan Deviation Approach." AIAA Modeling and Simulation Technologies Conference , no. : 1.
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.
Matthew B. Rhudy; Yu Gu; Jason Gross; Haiyang Chao. Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems. IEEE Transactions on Aerospace and Electronic Systems 2017, 53, 55 -66.
AMA StyleMatthew B. Rhudy, Yu Gu, Jason Gross, Haiyang Chao. Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems. IEEE Transactions on Aerospace and Electronic Systems. 2017; 53 (1):55-66.
Chicago/Turabian StyleMatthew B. Rhudy; Yu Gu; Jason Gross; Haiyang Chao. 2017. "Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems." IEEE Transactions on Aerospace and Electronic Systems 53, no. 1: 55-66.
Matthew Rhudy; Rungun Nathan. Integrated Development of Programming Skills Using MATLAB within an Undergraduate Dynamics Course. 2016 ASEE Annual Conference & Exposition 2016, 1 .
AMA StyleMatthew Rhudy, Rungun Nathan. Integrated Development of Programming Skills Using MATLAB within an Undergraduate Dynamics Course. 2016 ASEE Annual Conference & Exposition. 2016; ():1.
Chicago/Turabian StyleMatthew Rhudy; Rungun Nathan. 2016. "Integrated Development of Programming Skills Using MATLAB within an Undergraduate Dynamics Course." 2016 ASEE Annual Conference & Exposition , no. : 1.
Matthew Rhudy; Rungun Nathan. Fluids Friday! A Method for Improving Student Attentiveness and Learning in the Classroom. 2016 ASEE Annual Conference & Exposition 2016, 1 .
AMA StyleMatthew Rhudy, Rungun Nathan. Fluids Friday! A Method for Improving Student Attentiveness and Learning in the Classroom. 2016 ASEE Annual Conference & Exposition. 2016; ():1.
Chicago/Turabian StyleMatthew Rhudy; Rungun Nathan. 2016. "Fluids Friday! A Method for Improving Student Attentiveness and Learning in the Classroom." 2016 ASEE Annual Conference & Exposition , no. : 1.
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. 1. IntroductionAutonomous formation fight is an enabling technology for future manned and unmanned aircraft systems. Its potential benefits include energy savings and greenhouse gas reduction [1, 2], improved aircraft coordination within high density airspace [3, 4], and mixed operations of Unmanned Aerial Vehicles (UAVs) and manned aircraft [5]. Autonomous formation flight is also the foundation for autonomous aerial refueling [6] and UAV swarm operations [7].Close formation flight (CFF) is a natural and well documented phenomenon. Experimental biology research shows that certain birds earn 11.4% to 14.0% energy savings when flying in a “” shape formation [8, 9]. Similar benefits for fixed wing aircraft have also been investigated. In 2001, at NASA Dryden Flight Research Center, a demonstration of two F-18 research aircraft showed fuel savings of up to 14% during CFF [10]. In 2006 and 2013, a similar mission was conducted with multiple C-17 military aircraft which showed 10–14% fuel savings [11, 12]. This research and others [13] also showed that the trailing aircraft has to be precisely controlled at a specific location behind the leader’s wing tip to enjoy the energy savings. Therefore, precision computer control for close formation flight is a critical issue.Autonomous formation flight control has been explored using a number of different strategies such as “Multiple-Input-Multiple-Output,’’ “Leader-Follower,’’ “Cyclic,” and “Behavioral” [13]. Techniques for stability analysis of an autonomous formation have also been developed for measuring how position errors propagate form one vehicle to another in a cascaded system [14, 15].More specifically, the Leader-Follower approach has been widely accepted for aircraft formation flight due to relative simplicity where the problem can be represented as tracking problems that can be solved using standard control techniques. Compensation-type controllers [16–21], optimal control [22–25], adaptive control [26, 27], robust control [28], feedback linearization [27, 29–31], and behavioral [32] approaches have all been developed for formation flight applications for fixed wing aircraft and quadrotors.For this research, the nonlinear dynamic inversion (NLDI) control laws were inspired by the feedback linearization models of the early nineties [33, 34]. Feedback linearization is a generic description for the process of cancelling nonlinearity from all or a part of system’s differential equations to allow the use of linear approaches for controller design purposes. Input-output linearization describes the decomposition of those dynamics equations, a Multi-Input-Multi-Output (MIMO) system of equations, into linearized decoupled control laws [33]. Once simplified to linear functions, the equations can be inverted. This linearization and inversion process is known as nonlinear dynamic inversion. The main limitation of the approach is given by the necessary multiple assumptions made about the aircraft dynamics; therefore, the controller only performs as desired in a limited flight envelope. However, as shown in the technical literature, the flight envelope has been expanded greatly using adaptive control [14, 35–37], fuzzy logic [38], and neural network (NN) [34, 39] approaches.Experimental demonstrations of autonomous formation flight with fixed wing aircraft are very limited due to the complexity associated with multiple aircraft operations. Flight experimentation has been done by NASA [10, 40], DARPA [11], and academia [39, 41, 42].The research presented in this paper describes the latest results of a long-term research effort by researchers at West Virginia University (WVU) in demonstrating and analyzing autonomous close formation flight performance using small unmanned fixed wing and quadrotor aircraft. The control laws are designed using a similar method done with the YF-22 [41] and previous Phastball [43] aircraft flight test studies. This paper expands the analysis of the Phastball flight test analysis. It also adds the design of control laws, flight simulation, and performance analysis for a quadrotor platform.The main goal of this paper is to evaluate the performance of the designed formation controller from CFF flight test data. Another objective is to show the versatility of the control design by demonstrating close formation flight with two dynamically different platforms. In this effort, formation control performance is assessed and quantified by measuring how precisely the prespecified formation geometry can be maintained in level flight conditions.The paper is organized as follows. Section 2 provides a description of the formation flight controller designs. Section 3 explains the test bed designs. Section 4 discusses the simulation validation. Section 5 describes the Phastball flight testing and Section 6 discusses the experimental results. Section 7 concludes the paper with a discussion on future research directions.2. Formation Flight Controller Design2.1. Fixed Wing Controller DesignTwo WVU “Phastball” unmanned research aircraft fly in a tandem formation. The leader aircraft is flown by an operator on the ground. The follower aircraft is piloted by its onboard computer. Predetermined formation geometry is maintained by the flight control laws. The geometry is defined by vertical, , lateral, , and forward, , clearance from the leader’s GPS location. The orientation of the geometry is determined by the leader’s azimuth angle, , as illustrated in Figure 1.Figure 1: Formation flight geometry [43].The lateral, , forward, , and vertical, , distance errors are measured from the trailing aircraft’s desired position to its actual position:where , , and are the aircraft positions in a Local Tangent Plane (LTP) as measured by the GPS receivers. Leader parameters are indicated with the subscript “.” These errors are the performance criteria for analysis. An aircraft’s azimuth angle is calculated withwhere and are the aircraft velocity along -axis and -axis of LTP.The formation flight controller contains inner and outer feedback loop structure. The outer-loop controller minimizes the distance errors. It provides the desired pitch attitude, throttle position, and roll angle references to the inner-loop controller given its relative position with respect to the formation geometry. The inner-loop control laws then track these reference inputs by commanding the control actions, the aileron, rudder, elevator surfaces, and the motor speed.The flight path roughly lies on a horizontal 2D plane. This simplifies the flight control design into two decoupled sets of equations, one vertical and one horizontal.The outer-loop controller is designed using the NLDI approach. Two assumptions were made during the controller design process. First, the derivative of the flight path angle is assumed to be zero. Second, steady wings level or coordinated turn conditions are assumed for both the leader and follower aircraft. Detailed design for the outer-loop controller was presented in [41] and the developed nonlinear control laws for the horizontal tracking problem arewhere and are the desired roll angle and thrust commands, respectively. is mass (in kg). and are the angle of attack and side slip angle, respectively; is gravity; is the flight path angle; and is the aircraft angular turn rate. is the aircraft azimuth angle. and are constants to be provided by the engine model. and are the aerodynamic coefficients for drag. The linearized horizontal formation error dynamics, and , are equated from the following compensator-type linear control laws:Vertical geometry control is performed by a linear altitude tracker to produce the desired pitch angle:where is the desired pitch angle, is the vertical distance, and represents gains which are refined through simulation.The inner-loop control laws are designed with the goal of minimizing the cost function: and are the state variables of the aircraft and the optimized control action, respectively. The longitudinal states of include the angle of attack, ; pitch rate, ; and pitch angle, . Lateral-directional states of include side-slip angle, ; roll rate, ; roll angle, ; yaw rate, ; and yaw angle, . and are positive definite weighting matrices. The optimized control action, , enables the aircraft to track the desired outer-loop angles, pitch, , and roll, . The control action of the tracker is expressed asin the lateral direction and in the longitudinal direction, respectively. is the aileron surface deflection, is the rudder surface deflection, and is the elevator surface deflection. is the matrix of feedback gains associated with the difference between the desired outer-loop angles and the actual angles. is the matrix of feedback gains for the rest of the aircraft states. Simulation is used to affirm the inner-loop gains (8) and (9) and then, iteratively, for adjusting the outer-loop gains (5) and (6) to refine controller performance. The refined gains are shown in Table 1.Table 1: Phastball control gains.2.2. Quadrotor Controller DesignNEO quadrotors fly in a Leader-Follower configuration behind a leader as shown in Figure 2. The leader, red, can be a virtual or real object. The c
Caleb Rice; Yu Gu; Haiyang Chao; Trenton Larrabee; Srikanth Gururajan; Marcello Napolitano; Tanmay Mandal; Matthew Rhudy. Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds. International Journal of Aerospace Engineering 2016, 2016, 1 -15.
AMA StyleCaleb Rice, Yu Gu, Haiyang Chao, Trenton Larrabee, Srikanth Gururajan, Marcello Napolitano, Tanmay Mandal, Matthew Rhudy. Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds. International Journal of Aerospace Engineering. 2016; 2016 ():1-15.
Chicago/Turabian StyleCaleb Rice; Yu Gu; Haiyang Chao; Trenton Larrabee; Srikanth Gururajan; Marcello Napolitano; Tanmay Mandal; Matthew Rhudy. 2016. "Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds." International Journal of Aerospace Engineering 2016, no. : 1-15.
This article considers a novel approach to using global positioning system (GPS) signal strength readings and estimated velocity vector for estimating the attitude of a small fixed-wing unmanned aerial vehicle (UAV). This approach has the benefit being able to estimate full position, velocity and attitude states of a UAV using only the data from a single GPS receiver and antenna. Two different approaches for utilizing GPS signal strength within measurement updates for UAV attitude in a nonlinear Kalman filter are discussed and assessed using recorded UAV flight data. Comparisons of UAV pitch and roll estimates against measurements from a high-grade mechanical gyroscope are used to show that approximately 5° error with respect to both mean and standard-deviation on both axes is achievable.
Jason Gross; Yu Gu; Matthew Rhudy. Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements. Aerospace 2016, 3, 14 .
AMA StyleJason Gross, Yu Gu, Matthew Rhudy. Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements. Aerospace. 2016; 3 (2):14.
Chicago/Turabian StyleJason Gross; Yu Gu; Matthew Rhudy. 2016. "Fixed-Wing UAV Attitude Estimation Using Single Antenna GPS Signal Strength Measurements." Aerospace 3, no. 2: 14.
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.
Yu Gu; Jason Gross; Matthew B. Rhudy; Kyle Lassak. A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation. International Journal of Aerospace Engineering 2016, 2016, 1 -12.
AMA StyleYu Gu, Jason Gross, Matthew B. Rhudy, Kyle Lassak. A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation. International Journal of Aerospace Engineering. 2016; 2016 ():1-12.
Chicago/Turabian StyleYu Gu; Jason Gross; Matthew B. Rhudy; Kyle Lassak. 2016. "A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation." International Journal of Aerospace Engineering 2016, no. : 1-12.
This paper offers a set of novel navigation techniques that rely on the use of inertial sensors and wide-field optical flow information. The aircraft ground velocity and attitude states are estimated with an Unscented Information Filter (UIF) and are evaluated with respect to two sets of experimental flight data collected from an Unmanned Aerial Vehicle (UAV). Two different formulations are proposed, a full state formulation including velocity and attitude and a simplified formulation which assumes that the lateral and vertical velocity of the aircraft are negligible. An additional state is also considered within each formulation to recover the image distance which can be measured using a laser rangefinder. The results demonstrate that the full state formulation is able to estimate the aircraft ground velocity to within 1.3 m/s of a GPS receiver solution used as reference “truth” and regulate attitude angles within 1.4 degrees standard deviation of error for both sets of flight data.
Matthew B. Rhudy; Yu Gu; Haiyang Chao; Jason Gross. Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors. Journal of Robotics 2015, 2015, 1 -12.
AMA StyleMatthew B. Rhudy, Yu Gu, Haiyang Chao, Jason Gross. Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors. Journal of Robotics. 2015; 2015 ():1-12.
Chicago/Turabian StyleMatthew B. Rhudy; Yu Gu; Haiyang Chao; Jason Gross. 2015. "Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors." Journal of Robotics 2015, no. : 1-12.
This paper considers the fusion of carrier-phase differential GPS (CP-DGPS), peer-to-peer ranging radios, and low-cost inertial navigation systems (INS) for the application of relative navigation of small unmanned aerial vehicles (UAVs) in close formation-flight. A novel sensor fusion algorithm is presented that incorporates locally processed tightly coupled GPS/INS-based absolute navigation solutions from each UAV in a relative navigation filter that estimates the baseline separation using integer-fixed relative CP-DGPS and a set of peer-to-peer ranging radios. The robustness of the dynamic baseline estimation performance under conditions that are typically challenging for CP-DGPS alone, such as a high occurrence of phase breaks, poor satellite visibility/geometry due to extreme UAV attitude, and heightened multipath intensity, amongst others, is evaluated using Monte Carlo simulation trials. The simulation environment developed for this work combines a UAV formation flight control simulator with a GPS constellation simulator, stochastic models of the inertial measurement unit (IMU) sensor errors, and measurement noise of the ranging radios. The sensor fusion is shown to offer improved robustness for 3-D relative positioning in terms of 3-D residual sum of squares (RSS) accuracy and increased percentage of correctly fixed phase ambiguities. Moreover, baseline estimation performance is significantly improved during periods in which differential carrier phase ambiguities are unsuccessfully fixed. Note to Practitioners-This paper was motivated by the need to enhance the robustness of CP-DGPS/INS relative navigation. In particular, small UAVs exhibit fast dynamics and are often subjected to large and quickly changing bank angles. This in turn induces missed satellite observations and changes in the phase ambiguity. This paper suggests leveraging the emergence of Ultra Wideband ranging radios to directly observe the baseline separation. In this paper, we outline the details of the algorithm implementation. We then use a simulation to show that adding UWB greatly helps to enhance the robustness of the carrier ambiguity integer-resolving algorithm, which is necessary for improved solution accuracy. This work has extensions to ground vehicles, ocean buoys, and space vehicles. In future work, we will experimentally validate results.
Jason Gross; Yu Gu; Matthew B. Rhudy. Robust UAV Relative Navigation With DGPS, INS, and Peer-to-Peer Radio Ranging. IEEE Transactions on Automation Science and Engineering 2015, 12, 935 -944.
AMA StyleJason Gross, Yu Gu, Matthew B. Rhudy. Robust UAV Relative Navigation With DGPS, INS, and Peer-to-Peer Radio Ranging. IEEE Transactions on Automation Science and Engineering. 2015; 12 (3):935-944.
Chicago/Turabian StyleJason Gross; Yu Gu; Matthew B. Rhudy. 2015. "Robust UAV Relative Navigation With DGPS, INS, and Peer-to-Peer Radio Ranging." IEEE Transactions on Automation Science and Engineering 12, no. 3: 935-944.
Low-cost navigation can be performed for aircraft using the integration of Inertial Navigation System (INS) information in conjunction with a regulatory information source, such as Global Position System (GPS). For situations where GPS is unavailable, it is desirable to have an alternative information source in order to regulate the known drifting phenomenon experienced by INS. In this paper, the use of wide-field optical flow is explored to regulate INS drift for the purpose of GPS-denied navigation. An Unscented Information Filter (UIF) algorithm for Unmanned Aerial Vehicle (UAV) velocity and attitude estimation is proposed and evaluated with experimental flight data. The results demonstrate that the new filtering algorithm is capable of estimating the vehicle speed with approximately 1.4 m/s of error and regulating attitude errors to within 1.5 degrees standard deviation of error for both roll and pitch angles.
Matthew B. Rhudy; Haiyang Chao; Yu Gu. Wide-field optical flow aided inertial navigation for unmanned aerial vehicles. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems 2014, 674 -679.
AMA StyleMatthew B. Rhudy, Haiyang Chao, Yu Gu. Wide-field optical flow aided inertial navigation for unmanned aerial vehicles. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2014; ():674-679.
Chicago/Turabian StyleMatthew B. Rhudy; Haiyang Chao; Yu Gu. 2014. "Wide-field optical flow aided inertial navigation for unmanned aerial vehicles." 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems , no. : 674-679.
Matthew B. Rhudy; Yu Gu; Haiyang Chao. Wind Field Velocity and Acceleration Estimation Using a Small UAV. AIAA Modeling and Simulation Technologies Conference 2014, 1 .
AMA StyleMatthew B. Rhudy, Yu Gu, Haiyang Chao. Wind Field Velocity and Acceleration Estimation Using a Small UAV. AIAA Modeling and Simulation Technologies Conference. 2014; ():1.
Chicago/Turabian StyleMatthew B. Rhudy; Yu Gu; Haiyang Chao. 2014. "Wind Field Velocity and Acceleration Estimation Using a Small UAV." AIAA Modeling and Simulation Technologies Conference , no. : 1.
Matthew B. Rhudy; Yu Gu; Marcello R. Napolitano. Relaxation of Stability Requirements for Extended Kalman Filter Stability within GPS/INS Attitude Estimation. AIAA Guidance, Navigation, and Control Conference 2014, 1 .
AMA StyleMatthew B. Rhudy, Yu Gu, Marcello R. Napolitano. Relaxation of Stability Requirements for Extended Kalman Filter Stability within GPS/INS Attitude Estimation. AIAA Guidance, Navigation, and Control Conference. 2014; ():1.
Chicago/Turabian StyleMatthew B. Rhudy; Yu Gu; Marcello R. Napolitano. 2014. "Relaxation of Stability Requirements for Extended Kalman Filter Stability within GPS/INS Attitude Estimation." AIAA Guidance, Navigation, and Control Conference , no. : 1.
This paper presents modifications to the stochastic stability lemma which is then used to estimate the convergence rate and persistent error of the linear Kalman filter online without using knowledge of the true state. Unlike previous uses of the stochastic stability lemma for stability proof, this new convergence analysis technique considers time-varying parameters, which can be calculated online in real-time to monitor the performance of the filter. Through simulation of an example problem, the new method was shown to be effective in determining a bound on the estimation error that closely follows the actual estimation error. Different cases of assumed process and measurement noise covariance matrices were considered in order to study their effects on the convergence and persistent error of the Kalman filter.
Matthew B. Rhudy; Yu Gu. Online Stochastic Convergence Analysis of the Kalman Filter. International Journal of Stochastic Analysis 2013, 2013, 1 -9.
AMA StyleMatthew B. Rhudy, Yu Gu. Online Stochastic Convergence Analysis of the Kalman Filter. International Journal of Stochastic Analysis. 2013; 2013 (2):1-9.
Chicago/Turabian StyleMatthew B. Rhudy; Yu Gu. 2013. "Online Stochastic Convergence Analysis of the Kalman Filter." International Journal of Stochastic Analysis 2013, no. 2: 1-9.