Estimation of Kalman filter gain from output residuals

Fixed-point FPGA Implementation of a Kalman Filter for Range and Velocity Estimation of Moving Targets

Tracking filters are extensively used within object tracking systems in order to provide consecutive smooth estimations of position and velocity of the object with minimum error. Namely, Kalman filter and its numerous variants are widely known as simple yet effective linear tracking filters in many diverse applications. In this paper, an effective method is proposed for designing and implementa...

Bayesian Estimation and the Kalman Filter

In this tutorial article we give a Bayesian derivation of a basic state estimation result for discrete-time Markov process models with independent process and measurement noise and measurements not a ecting the state. We then list some properties of Gaussian random vectors and show how the Kalman ltering algorithm follows from the general state estimation result and a linear-Gaussian model de n...

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...