Recursive least square based estimation of MEMS inertial sensor stochastic models

Abstract

In this paper we first analyze the effects of least square based parameter estimation for a autoregressive stochastic model of inertial sensor errors. We then proceed to develop the recursive least squares (RLS) estimation of the autoregressive model parameters and also discuss a fast update method for recursive least square estimation to reduce the computation complexity. This reduction leads to an efficient online dynamic estimation of inertial sensor error model which can then augment a navigation system based on such sensors. Simulation results and actual inertial sensor data are analyzed and it is shown that the RLS estimate can achieve a 20% reduction in forward prediction error as compared to the non-recursive estimate.