Detection and Filtering of Short-Term (1 /fγ) Noise in Inertial Sensors

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Abstract
Combining inertial navigation system (INS) and GPS data by means of Kalman filtering suppresses sensor noise only in a limited frequency band. This paper describes a filtering methodology that allows optimal elimination of the inertial sensor noise over the whole frequency band of interest. This methodology accommodates a model for short-term inertial errors that is especially applicable to ring-laser and fiber-optic gyro technologies. The paper presents a technique for adaptive estimation of the model parameters based solely on data analysis above the edge of the frequency band corresponding to the motion. Using this technique, the model parameters are estimated for a specific system and time period, the noise distribution is predicted within the band of interest, and the inertial measurement errors are reduced in the wavelet domain. This methodology complements the standard approach of Kalman filtering/smoothing. Empirical testing using different navigation-grade strapdown inertial systems shows an improvement of 20 percent in attitude determination under dynamic conditions.

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