Fast Estimation for the Long Data Chain by the Spring Force Model

It is very important to carry out a fast estimation method for long data chain due to the online purpose or the requirement of low compute cost. This paper combines the motion characteristics with the Kalman estimate algorithm to achieve this goal. Measurements are determined according the accelerations of the moving objects so that fulfil the estimation by an irregularly sampling concept. Thus the spring force model is provided to select the measurement for estimation by finding the relation between the acceleration and the next estimation data. The simulate results show that our spring force model can carry out fast estimation with good tracking performance.