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13 October 2008 Application of the least square filtering in initial alignment of SINS
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When the statistics of the system noise and the observation noise are unknown, or almost unknown, the state estimation error computed by Kalman Filtering will be much bigger, or the Kalman Filter may become divergence. In order to avoid this demerit, a Least Square Filtering is presented. It weighs the observation data adaptively only without the requirement of the statistics of the noise. This algorithm is used to Strapdown Inertial Navigation System (SINS) initial alignment and compared with the Kalman Filtering. The simulation results show that the Least Square (LS) Filtering has faster convergent speed than the Kalman Filtering.
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Long Zhao and Li Wang "Application of the least square filtering in initial alignment of SINS", Proc. SPIE 7128, Seventh International Symposium on Instrumentation and Control Technology: Measurement Theory and Systems and Aeronautical Equipment, 712816 (13 October 2008);

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