Statistical modeling of additive noise and random drift for triaxial rate gyros and accelerometers

Abstract

Micro Electro Mechanical Systems (MEMS) gyroscopes and accelarometers are a new type of inertial sensor with small size, light-weight, low-cost and low power consumption. Thus, it has been widely used for guidance and stabilization of many platforms. Although these MEMS devices have such crucial advantages, two important statistical parameters which are the spectral densities R and Q of the additive noise and random drift, respectively, may suddenly degrade the system performance in a short period of time. Therefore, a suitable modeling of these errors is vital to guarantee the system performance. The previous works based on Allan variance (AV)(time domain analysis) or Power Spectral Density (PSD)(frequency domain analysis) mainly deal with univariate situations, and only give a suitable modeling of uniaxial sensors signal, not give a precise estimation for an array of gyros or accelerometers. In practical engineering, most the MEMS sensors are applied to an array form, especially the triaxial gyros. Aiming at application in practical engineering and dealing with triaxial rate gyros and accelerometers additive noise and random drift, this paper provides a statistiacl estimating algorithm for jointly estimating R and Q. The performance of the algorithm is demonstrated using both simulated data and experimental data.