Noise variance estimation for Kalman filter

Adaptive Kalman Filter for Noise Identification

This paper deals with an on line identification of the values of these entities by a suitable adjusting of the filter gain and incorporating information about the quality of innovation sequence. The result yields a new adaptive Kalman filter. The proposal is mainly based on statistical properties of the innovation sequence of the filter and improves some previous results pointed out by Bellange...

The Unscented Kalman Filter for Nonlinear Estimation

The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the ExpectationMaximization (EM) algorithm)where both s...

Indirect Kalman Filter for 3D Attitude Estimation

Kalman Filter for Spinning Spacecraft Attitude Estimation

This paper presents a Kalman filter using a seven-component attitude state vector comprising the angular momentum components in a n inertial reference frame, the angular momentum components in the body frame, and a rotation angle. The relatively slow variation of these parameters makes this parameterization advantageous for spinning spacecraft attitude estimation. The filter accounts for the co...

Unscented Kalman Filter for Vehicle State Estimation

Vehicle Dynamics Control (VDC) systems require the information about system variables, which cannot be directly measured, e.g., the wheel slip or the vehicle side-slip angle. This paper presents a new concept for the vehicle state estimation under assumption that the vehicle is equipped with the standard VDC sensors. It is proposed to utilize an Unscented Kalman Filter (UKF) for estimation purp...