Design and implementation of an adaptive Kalman filtering for the launcher of multiple launch rocket system

Practical application for performing an adaptive Kalman filtering with a digital signal processor is studied. A multiple launch rocket system (MLRS) is considered, and an adaptive Kalman filtering is designed for the state estimation of the launcher by using the measured outputs from a fiber optical gyro mounted on the MLRS. The proposed algorithm remains convergent in the presence of noise covariance errors. The performance of the proposed method is demonstrated by simulations and compared with other types of Kalman filtering algorithms. Eventually, an experiment is implemented for the state estimation of the launcher of MLRS using the TMS320F2812 digital signal processor.