High Precision Attitude Estimation Algorithm of Mems Array in Uavs for 6G Iot

With the rapid development of 6G technology, the space-air-ground-sea integrated network has become an important trend in the future development of the Internet of Things. In this context, UAV, with its high degree of flexibility and mobility, has become a key node, which can realize multi-dimensional sensing, calculation and communication functions in sky, air, ground and sea. In order to improve the attitude estimation accuracy of UAV, a two-stage data fusion attitude estimation algorithm based on MEMS multi-sensor is proposed in this paper. In the first stage, a simplified Kalman filter (KF) algorithm is proposed to fuse the measurement data of MEMS gyroscope array. In the second stage, two-stage Kalman filter is used to filter the fused angular rate, accelerometer and magnetometer data with quaternion as the state quantity, and finally the attitude Angle estimation is realized with high precision through multisensor data fusion. Experimental results show that the performance of the algorithm on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{Z}$</tex>-axis is significantly improved: angular random walk (ARW), rate random walk (RRW) and zero-bias instability are reduced by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7.57 ~\text{dB}, 3.26 ~\text{dB}$</tex> and 2.48dB, respectively. Under static conditions, the standard deviations of roll Angle, pitch Angle and yaw Angle are reduced by 5.02dB, 2.28dB and 4.60dB respectively. Under dynamic conditions, the standard deviations of roll Angle, pitch Angle and yaw Angle are reduced by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.50 ~\text{dB}, 4.05 ~\text{dB}$</tex> and 2.14dB respectively. These results fully verify the feasibility and reliability of the proposed algorithm, and provide strong support for high-precision attitude estimation of UAVs in complex environments.