Sequential multiscale Kalman filtering algorithm for multisource combinatorial navigation

With the rapid development of UAVs, the reliability and safety of the navigation system become critical. The traditional navigation system has the problem of the defective positioning effect of a single sensor and scale, in order to improve the stability and reliability of the navigation system, this paper proposes a sequential multi-scale Kalman filter algorithm (SMSKF) for multi-source combined navigation. Firstly, the original state equations of the system described in the time domain are converted into block state equations by using the chunking technique and wavelet transform, and then the measurement vectors of the current moment obtained in real-time are expressed in the form of block state vectors, and at the same time, Kalman filtering technique and sequential filtering idea are combined, and finally, experimental validation is carried out for the algorithm, and the result proves the effectiveness of the method.