Attitude determination for low cost imu and processor board using the methods of triad, kalman filter and allan variance

As future work, Kalman filter inputs can be added such as the previous known scanned magnetic field, accelerometer/gyroscope and visual localization to improve the localization of

In Figure 3 a)–b) the directly online estimated fre- quency responses are shown for both reference systems A and B when noise is affecting the system output and also in the

Algumas dessas mudanças são utilizadas neste trabalho; sendo elas o Filtro de Kalman Estendido (EKF), Unscented Kalman Filter (UKF) e Filtro de Kalman de Cubagem Esférica

Since the inter- fering signals may not be stationary, a substitute model based on recurrent neural network (RNN) structure is more suitable for nonstationary signal prediction

Given a discrete-time nonlinear stochastic system, as the presented in equation (1), the maximum likelihood estimation technique can be used to find the unknown parameters θ , of

ABSTRACT: In this study, an Unscented Kalman Filter (UKF) algorithm is designed for estimating the attitude of a pico- satellite and the in-orbit external disturbance torques..

he method to estimate the attitude used is Unscented Kalman Filter (UKF), since this estimator is capable of performing state estimation in non-linear systems, besides taking

The estimation algorithm considers a Kalman filter with a rather simple orbit dynamic model and random walk modeling of the receiver clock bias