Statistical confidence domain data driven based fast in-flight alignment method

Initial in-flight alignment is the basis of accurate navigation for projectiles strap-down inertial navigation system (SINS). Due to complex and highly dynamic flight environment of projectiles, inertial sensors and GNSS are susceptible to interference, which causes measurement noise appear as non-Gaussian noise, resulting in low alignment accuracy and long alignment time. Thus, this paper proposes statistical confidence domain data driven based fast in-flight alignment method. Firstly, the K matrix is used as state variables to construct projectiles initial in-flight alignment filter model. The noise evaluation indexes are defined according to measurement information and estimation results to judge abnormal degree of measurement noise. Based on this, we propose an adaptive robust matrix Kalman filter (ARMKF) method. The measurement variance matrix formulas are derived based on additive noise, which provides theoretical support for parameter selection in practical applications. Simulation and test results show that alignment accuracy and alignment time of the proposed method are better than traditional methods.

Automated summary

This paper proposes a statistical confidence domain data driven based fast in-flight alignment method to improve the alignment accuracy and alignment time by using K matrix to construct a filter model and defining noise evaluation indexes.