Identification of models and adaptive filtering of inertial sensors noises

Аuthors

Chernodarov A. V.^1*, Ivanov S. A.^2**

1. Leading Researcher of “NaukaSoft” Experimental Laboratory, 9, Godovikova str., building 1, Moscow, 129085, Russia
2. Ramenskoye Instrument-Making Plant, 39, Mikhalevich str., Ramenskoe, Moscow Region, 140100, Russia

*e-mail: chernod@mail.ru
**e-mail: srpremier@mail.ru

Abstract

This article is devoted to the problem of reliability increasing of of inertial measurement units (IMU) error estimation by an extended Kalman filter (EKF). This problem is associated with the noise models of inertial sensors and real processes inadequacy. Gyroscopes and accelerometers are considered as inertial sensors. It is known, that the models inaccuracy and other causes of methodical and instrumental character result in the EKF divergence. The EKF divergence manifests itself in the fact that the actual estimation errors considerably differ from their predicted mean square values obtained while solving the Riccati equation for the covariance matrix. It should be noted, that the actual estimation errors come to light only at the stage of mathematical simulation. In the known works the models inadequacy is compensated by the corresponding EKF parameters adjustment over the renovative sequence. This renovative sequence is the difference between actual and predicted observations. The predicted observations are formed by estimating the IMU errors. However, in real operating conditions, due to the errors of external measuring tools or lack thereof, such adjustment is not always possible. In addition, there are approaches to estimation of statistical characteristics of inertial sensors by the Allan method. This method allows estimate the stability of errors on the moving intervals of averaging. However, such approaches do not associate with the EKF parameters tuning. Thus, their application does not ensure the EKF adaptation in real operating conditions. The scientific originality of the proposed work is associated with the addition of procedures for the noise models of inertial sensors tuning to the known EKF adaptation algorithms. The authors propose to perform the adjustment of models based on structural-parametric identification by of correlation processing of the sensors error estimates. Such processing can be performed both in real time, and according to the data of onboard recording devices. The developed algorithms allow take account for the change in accuracy and dynamic characteristics of inertial sensors through the corresponding coefficients of the IMU errors. To implement the proposed identification technology, the errors of inertial sensors should be included in the estimated parameters vector. The performed studies revealed that when the EKF is included in the IMU error estimation circuit, it is necessary to perform not only the factory bench calibration of inertial sensors, but also the identification of their noise models. The article presents the results of experiments confirming the expediency of noises identifying models of inertial sensors while operation.

Keywords:

inertial navigation system, sensors, noise models, identification, Kalman filter

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