%0 Journal Article
%T An Intuitive Approach to Inertial Sensor Bias Estimation
%A Vasiliy M. Tereshkov
%J International Journal of Navigation and Observation
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/762758
%X A simple approach to gyro and accelerometer bias estimation is proposed. It does not involve Kalman filtering or similar formal techniques. Instead, it is based on physical intuition and exploits a duality between gimbaled and strapdown inertial systems. The estimation problem is decoupled into two separate stages. At the first stage, inertial system attitude errors are corrected by means of a feedback from an external aid. In the presence of uncompensated biases, the steady-state feedback rebalances those biases and can be used to estimate them. At the second stage, the desired bias estimates are expressed in a closed form in terms of the feedback signal. The estimator has only three tunable parameters and is easy to implement and use. The tests proved the feasibility of the proposed approach for the estimation of low-cost MEMS inertial sensor biases on a moving land vehicle. 1. Introduction In most inertial measurement devices, for example, attitude and heading reference systems (AHRS), the output performance suffers from gyro and accelerometer biases. Uncompensated portions of these biases result in unbounded accumulation of attitude errors. A classical approach to AHRS correction, dated back to the 1930s, is attitude error elimination by means of an external aid that provides vehicle acceleration data [1, 2]. In this scheme, the differences between the accelerometer measurements and the true accelerations are treated as gravity projections proportional to the AHRS stable platform tilt errors. The platform gyros are torqued until these errors vanish and the platform returns to the level plane. This approach is also directly applicable to a strapdown AHRS if a notion of a ¡°virtual platform¡± is introduced [3, 4]. The direction cosine matrix updated by the AHRS computer represents the orientation of the vehicle relative to a ¡°virtual platform,¡± and the angular motion of the vehicle is the difference of its absolute rotation and the motion of that ¡°platform.¡± Even though the correction procedure prevents the accumulation of attitude errors, it cannot completely zero them, as inertial sensor biases remain uncompensated. In modern studies, Kalman filtering [5] is usually preferred as a more general and powerful approach to aided inertial system design. The state vector to be estimated is composed of output errors and then, if necessary, is augmented by inertial sensor biases [6, 7]. Thus, the phases of attitude correction and sensor error estimation are replaced with a single update procedure of the Kalman filter. Though very popular, Kalman filtering has
%U http://www.hindawi.com/journals/ijno/2013/762758/