Coning algorithm design by batch-processing

Abstract

Coning algorithm is the kernel during attitude algorithm design in strapdown inertial navigation. A new approach is presented for strapdown coning algorithm design based on batch-processing. Unlike previous time or frequency Taylor series expansion techniques, the new method achieves the rotation vector through discrete Fourier transforms(DFT) and inverse Fourier transforms(IFT) of the inputs. This methodology allows the coning algorithm to be design in frequency, which might be a novel aspect to the coning algorithm. Performance of the new algorithm design technique is evaluated by comparison with previous time approaches, typically four-sample algorithm, in pure coning environments. Simulation results indicate that the new method reduces the coning error when the coning frequency is under 100Hz. The new method might be an alternate method during ship navigation where the coning frequency is not very high.