Abstract—State estimation theory is one of the best mathematical approaches to analyze the changes in the states of a system or a process. The state of the system is defined by a set of variables that provide a complete representation of the internal conditions of the system at any given time instant. There are two types of state models - linear state model and non-linear state model, which require different estimation techniques. Linear estimation of a system can be easily carried out by using Kalman Filter (KF), when the state space model is linear. But, most of the real life state models are nonlinear, thereby limiting the practical applications of the KF. The Extended Kalman Filter, Unscented Kalman filter and Particle filter are most commonly used for nonlinear estimation. EKF is the nonlinear version of the Kalman filters which revolves about the mean and covariance at the current time instant. The estimation can be linearized around the current estimate using the partial derivatives to compute estimates even in the nonlinear relations. This paper deals with estimation of various parameters of a nonlinear model with Extended Kalman filter (EKF).The paper analyses EKF method of estimating and then determining the attitude of the satellite depending upon the readings from the magnetometer.

Index Terms—extended Kalman filter, estimation, attitude control, state space model, nano-satellite, magnetometers

Cite: Sai Dheeraj Nadella, "Use of Extended Kalman Filter in Estimation of Attitude of a Nano-Satellite," International Journal of Electronics and Electrical Engineering, Vol. 3, No. 1, pp. 38-43, February 2015. doi: 10.12720/ijeee.3.1.38-43