%0 Electronic Article
%A Hailong Rong
%A Ling Zou
%A Cuiyun Peng
%A Jidong Lv
%A Yang Chen
%A Yaping Zhu
%K Kalman filter
%K hidden Markov model
%K maximum attitude estimation error
%K MTi
%K magnetometer
%K MIMU
%K weight adaptive regulation method
%K REQUEST algorithm
%K Allan variance
%K accelerometer
%K recursive quaternion estimation algorithm
%K weight fusion
%K attitude calculation algorithm
%K gyroscope
%K magnetic and inertial measurement unit
%K three-dimensional random movement
%X An attitude algorithm is used by a given magnetic and inertial measurement unit (MIMU) for weight fusion of the outputs of gyroscope and the outputs of the subunit constructed by accelerometer and magnetometer for attitude calculation of a rigid body. It has been proved that the weight assigned to the outputs of gyroscope determines the overall performances of attitude algorithm. A new weight adaptive regulation method based on Kalman filter and hidden Markov model is proposed, and then a new attitude algorithm is constructed by the integration of that method with recursive quaternion estimation algorithm. The authors’ attitude algorithm is compared with some other related attitude algorithms in their experiments. With the outputs of the sensors in MTi as the inputs of the new attitude algorithm, the maximum attitude estimation errors in response to three-dimensional random movements and the changes of their amplitudes are 2.85° and 6.82°, respectively, and the Allan variance of attitude drift in response to motionless conditions is 2.1×10−4°. Other attitude algorithms cannot simultaneously achieve similar dynamic and similar static performances comparing to their attitude algorithm. The results validate the overall performance improvement of their attitude algorithm.
%@ 1751-8822
%T Adaptive regulation of the weights of REQUEST used to magnetic and inertial measurement unit based on hidden Markov model
%B IET Science, Measurement & Technology
%D August 2018
%V 12
%N 5
%P 666-672
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=9o7s1n8u0r59.x-iet-live-01content/journals/10.1049/iet-smt.2017.0383
%G EN