A novel application of adaptive filtering for initial alignment of Strapdown Inertial Navigation System

A novel method for improving the alignment accuracy and convergence speed of strap down inertial navigation system has been reported in this paper. From the dynamic model, the Strapdown Inertial Navigation System (SINS) error model is derived for a small misalignment angle. Many algorithms are proposed for proper estimation of alignment accuracy for INS. Keeping this view in mind we propose two novel algorithms viz. Volterra Recursive Least Square (VRLS) and Bilinear Recursive Least Square (BRLS) based on nonlinear adaptive filter for the estimation of accurate azimuth alignment error. The comparative performances of the aforesaid algorithms are presented to show the effectiveness of the proposed (BRLS) algorithm. The effective performance of proposed BRLS algorithm in presence of three different white Gaussian noise with is studied for the first time in MATLAB simulating environment. The comparative analysis is precisely presented to realize the validation of proposed BRLS algorithm which is compared with recently proposed Kalman filter and Sage-Husa algorithms. The proposed BRLS algorithm enhances the convergence rate of azimuth attitude error and alignment accuracy effectively.