The performance of tactical weapons inertial navigation system (INS) greatly depends on the accuracy and rapidity of transfer alignment (TA). The major challenge is the estimation of the attitude of INS rapidly and accurately, under the condition of random initial misalignment angles and launch time constraint in tactical scenarios. The objective of this paper is to derive a unified nonsingular alignment scheme for rapid TA at random initial misalignment angles, which transforms the alignment problem into the estimation of the relative attitude matrix between the master INS and slave INS. Employing the attitude matrix as estimate state, we formulate a unified nonsingular linear dynamics model allowing us to estimate the attitude matrix directly. Furthermore, the attitude matrix estimation algorithm has been extended to work in a state matrix Kalman filter (MKF) framework, which we call MKFTA algorithm. It works without any assumption on the misalignment angles. Finally, Monte Carlo simulations demonstrate that the MKFTA can converge within 10 s and exhibits a better transient and steady-state accuracy than the traditional scheme.
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