A filtering approach based on MMAE for a SINS CNS integrated navigation system

Abstract

This paper explores multiple model adaptive estimation &#x0028 MMAE &#x0029 method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter — multiple model adaptive estimation unscented Kalman filter &#x0028 MMAE-UKF &#x0029 rather than conventional Kalman filter methods, like the extended Kalman filter &#x0028 EKF &#x0029 and the unscented Kalman filter &#x0028 UKF &#x0029. UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters, which the improved filtering method can overcome. Meanwhile, this algorithm is used for integrated navigation system of strapdown inertial navigation system &#x0028 SINS &#x0029 and celestial navigation system &#x0028 CNS &#x0029 by a ballistic missile’s motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.