Altitude data fusion utilising differential measurement and complementary filter

Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

“…Figure 6 shows that the gyro-based attitudes derived from quaternion and Euler algorithm have short-term accuracy, which suffers from long-term accumulated errors caused by gyroscope bias drift with reference to (5) and (16). The attitude algorithm by accelerometer could provide roll and pitch estimation of long-term accuracy but suffers from sensor errors and vibration noises due to inevitable disturbances and dynamic motions with reference to (10). The pseudoattitude algorithm based on GPS could provide heading angle as measurement yaw angle via GPS-measured velocity with reference to (17); its maximal measured error of yaw even reaches 4.65 degree in turning section.…”

“…For attitude estimation algorithm by fusing data from MEMS inertial sensors, complementary filter (CF) is usually utilized to estimate attitudes due to the complementary characteristic of gyroscopes and accelerometer [10,11]. The classical form of the complementary filters can be reformulated.̂(…”

“…The vector = . Accelerometers can provide the pitch and roll angles by measuring the gravity vectors if the vehicle is stationary or in low dynamic environment in (10). Triaxis magnetometers are used for calculating the yaw angle in (14) and (15).…”

“…Nowadays, the different attitude estimation algorithms have been developed, which are employed to Micro IMUs and integrated with magnetic sensors, Global navigation satellite system (GNSS) receiver, and other sensors [1][2][3][4][10][11][12][13][14][15][16][17]. Among them, representative researches like multiinformation fusion technique utilize the different MEMS sensor [10][11][12], different Kalman filtering methods for attitude estimation [3,[13][14][15], and methodology combining data fusion with filtering strategies [16,17].…”

“…Among them, representative researches like multiinformation fusion technique utilize the different MEMS sensor [10][11][12], different Kalman filtering methods for attitude estimation [3,[13][14][15], and methodology combining data fusion with filtering strategies [16,17]. Utilizing barometer and low cost IMU, the data fusion method via the complementary filter is proposed to fuse altitude data for unmanned aerial vehicle (UAV) system [10].…”