In this paper, a new method based on optimal combination of inertial sensors; in process of initial alignment for strapdown navigation system; is proposed. The equations of initial alignment are usually based on accelerometer outputs and/or gyroscope outputs, depending on sensor’s accuracy. Our initial alignment algorithm leads to linear combination of output vectors. Although the error of this method is independent of sensor’s biases, unfortunately the coefficient of this combination is unknown. Knowledge of designer or sensor’s accuracy is a normal solution, but that obviously will not lead to the best estimation. The proposed idea is utilizing genetic algorithm to achieve optimal combination of sensors. In this regard, the optimal transformation matrix must be estimated, and the performance index is a function of alignment error. Final result of optimization problem is the best coefficient to combine outputs. The simulation results show excellent performance of proposed algorithm.
Omidi Hemmat,O. and Nikkhah,A. A. (2015). Optimal combination of inertial sensors for initial alignment of strapdown navigation system using genetic algorithm. Journal of Aeronautical Engineering, 17(1), 22-34.
MLA
Omidi Hemmat,O. , and Nikkhah,A. A. . "Optimal combination of inertial sensors for initial alignment of strapdown navigation system using genetic algorithm", Journal of Aeronautical Engineering, 17, 1, 2015, 22-34.
HARVARD
Omidi Hemmat O., Nikkhah A. A. (2015). 'Optimal combination of inertial sensors for initial alignment of strapdown navigation system using genetic algorithm', Journal of Aeronautical Engineering, 17(1), pp. 22-34.
CHICAGO
O. Omidi Hemmat and A. A. Nikkhah, "Optimal combination of inertial sensors for initial alignment of strapdown navigation system using genetic algorithm," Journal of Aeronautical Engineering, 17 1 (2015): 22-34,
VANCOUVER
Omidi Hemmat O., Nikkhah A. A. Optimal combination of inertial sensors for initial alignment of strapdown navigation system using genetic algorithm. JoAE, 2015; 17(1): 22-34.
