Imam Hussein University Aerospace Mechanics 2645-5323 16 1 2020 03 20 Data Fusion of Gyroscope and Magnetometer to Estimate the Attitude of High-Speed Projectiles Based on Particle Filter and PSO Data Fusion of Gyroscope and Magnetometer to Estimate the Attitude of High-Speed Projectiles Based on Particle Filter and PSO 15 30 205045 FA Ali Asghari Department of Electrical and Computer Engineering Malek-Ashtar University of Technology, Tehran,Iran 0009-0004-0719-1839 Saeed Nasrollahi Department of Electrical and Computer Engineering, Malek-Ashtar University of Technology, Tehran, Iran 0000-0002-8275-8514 Nematollah Ghahremani Department of Electrical and Computer Engineering Malek-Ashtar University of Technology, Tehran,Iran 0000-0001-5932-0891 Journal Article 2019 05 26 In this paper, the attitude of high-speed projectiles has been estimated usingdata fusion of magnetometer and Micro Electro Mechanical Systems (MEMS) gyroscope. MEMS gyroscopes have the high error for high speed. Also, magnetometers have low accuracy due to the presence of Non-Earth magnetic fields. For this reason, data fusion of magnetometer and MEMS gyroscope have been suggested. Due to the nonlinearity of the system equations and observation, a nonlinear estimator must be used. The developed Kalman filter inserts an error by ignoring the high order sentences of Taylor's expansion, which cannot be ignored in fast nonlinear systems. Unlike the Kalman filter, the particle filter has good results for nonlinear systems. The biggest weakness of this filter is its high computational time, which limits its applicability. To reduce the computational time of particle filter, a particle swarm optimization algorithm has been used. The simulation results were evaluated using 100 samples of the test, which illustrates the desirable performance of the combined particle filter with the particle swarm optimization algorithm in the data fusion of gyroscope and magnetometer information in the estimation of angles. In this paper, the attitude of high-speed projectiles has been estimated usingdata fusion of magnetometer and Micro Electro Mechanical Systems (MEMS) gyroscope. MEMS gyroscopes have the high error for high speed. Also, magnetometers have low accuracy due to the presence of Non-Earth magnetic fields. For this reason, data fusion of magnetometer and MEMS gyroscope have been suggested. Due to the nonlinearity of the system equations and observation, a nonlinear estimator must be used. The developed Kalman filter inserts an error by ignoring the high order sentences of Taylor's expansion, which cannot be ignored in fast nonlinear systems. Unlike the Kalman filter, the particle filter has good results for nonlinear systems. The biggest weakness of this filter is its high computational time, which limits its applicability. To reduce the computational time of particle filter, a particle swarm optimization algorithm has been used. The simulation results were evaluated using 100 samples of the test, which illustrates the desirable performance of the combined particle filter with the particle swarm optimization algorithm in the data fusion of gyroscope and magnetometer information in the estimation of angles. https://maj.ihu.ac.ir/article_205045_d54699da33fa854ac46f4729ae3f216b.pdf