Fusion of GPS and Redundant IMU Data for Attitude Estimation

Abstract

Attitude estimation using Global Positioning System/Inertial Navigation System (GPS/INS) was used as an example application to study three different methods of fusing redundant multi-sensor data used in the prediction stage of a nonlinear recursive filter. Experimental flight data were collected with an Unmanned Aerial Vehicle (UAV) containing GPS position and velocity calculations and four redundant Inertial Measurement Unit (IMU) sensors. Additionally, the aircraft roll and pitch angles were measured directly with a high-quality mechanical vertical gyroscope to be used as a 'truth' reference for evaluating attitude estimation performance. A simple formulation of GPS/INS sensor fusion using an Extended Kalman Filter (EKF) was used to calculate the results for this study. Each of the three presented fusion methods was shown to be effective in reducing the roll and pitch errors as compared to corresponding results using single IMU GPS/INS sensor fusion. Additionally, the fusion methods were shown to be effective in estimating roll and pitch angles without the aid of GPS (dead reckoning). NomenclatureA = state prediction Jacobian matrix a x , a y , a z = acceleration in aircraft body frame (m/s 2 ) f = state prediction function g = acceleration due to gravity (m/s 2 ) H = observation matrix I = identity matrix K = Kalman gain matrix k = discrete time index P = state covariance matrix p = roll rate (deg/s) Q = process noise covariance matrix q = pitch rate (deg/s) R = measurement noise covariance matrix r = yaw rate (deg/s) T = averaging time (s) T s = sampling time (s) u = input vector v = measurement noise vector V x , V y , V z = local components of velocity (m/s) W = random walk coefficient w = process noise vector x = state vector y = output vector z = measurement vector 2 θ = pitch angle (deg) σ A = root Allan variance ϕ = roll angle (deg) ψ = yaw angle (deg)

Unmanned Aerial Vehicle Navigation Using Wide-Field Optical Flow and Inertial Sensors

“…Note that mean errors in the roll and pitch estimation could be due to misalignment between the vertical gyroscope, IMU, and video camera. The attitude estimation accuracy is reported in Tables 2 and 3 similar to the reported accuracy of loosely coupled GPS/INS attitude estimation using similar flight data [43].…”

“…The research platform used for this study is the West Virginia University (WVU) "Red Phastball" UAV, shown in Figure 2, with a custom GPS/INS data logger mounted inside the aircraft [28,43]. Some details for this aircraft are provided in Table 1.…”

Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs

“…The Phastball SUAV airframe [43] was developed as a modular research platform and has been used for multiple sensorfusion studies [44][45][46]. The Phastball Zero SUAV is shown in Figure 3.…”

Implementation of A low-cost multi-IMU hardware by using a homogenous multi-sensor fusion

“…The prototype of the hardware presented in Fig.3. The sensors arranged so that their sensitive axes (X,Y) are in opposite direction of each other [9]. It was demonstrated in [10] with this designation of placing sensors, common mode errors like bias drift with temperature would at least partially will be removed.…”