Stochastic modeling is a challenging task for low-cost inertial sensors whose errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. We are currently investigating two approaches for bounding the errors in the mechanization. The first is an improved modeling of stochastic errors through the superposition of several Auto-Regressive (AR) processes. A new algorithm is presented based on the Expectation-Maximization (EM) principle that is able to estimate such complex models. The second approach focuses on redundancy through the use of multiple IMUs which don't need to be calibrated a priori. We present a synthetic IMU computation in which the residuals are modeled by a single ARMA model. The noise power issued from the residuals is then continuously estimated by a GARCH model, which enables a proper weighting of the individual devices in the synthetic IMU.