Sensor fusion in remote sensing satellites using a modified Kalman filter

The demand for remote sensing satellites is ever increasing due to their potential applications in many areas such as agriculture, forestry, water resources and so on. One of the key concerns in remote sensing satellite applications is that the accuracy of the attitude and orbit control must be maintained at a high level. In general applications, measurement of the attitude using only one type of sensor is satisfactory. However, in applications where a high precision of attitude control is required, several types of sensor need to be integrated together to ensure high measurement accuracy. In these cases, data fusion is required to process the information collected from different sensors. The least mean square method is commonly used for data fusion. This method is simple to use but not accurate enough. The Kalman filter (KF) is an alternative choice, which produces more accurate results, but a conventional KF fails to ensure error convergence due to limited knowledge of the system's dynamic model and measurement noise. In this paper, a modified KF is proposed to overcome the error divergence problem. The possible decrease in the signal processing results which appears in the classical KF can be avoided. The effectiveness of this approach has been demonstrated through a simulation study of the attitude measurement by the attitude and orbit control system of a remote sensing satellite used by the China Astronautic Board.