Incremental Kalman filter method under poor- observation condition parameters

An incremental Kalman filter method under poor observation condition is put forward, in which its concept, model, basic equations and key calculative steps are given. Classical Kalman filter method requires a high precision measurement equation, or else it will cause great error in the recursive process. However, the measurement equation is usually influenced by environmental factors, and many practical situations are called poor observation conditions in which the measurement equation cannot be verified or calibrated. There usually are unknown system errors of the measurement equation under the poor observation conditions (such as deep space exploration), which lead to great Kalman filter errors. The presented incremental Kalman filter method can successfully eliminate these unknown system errors and greatly improve the precision of Kalman filter. The method is simple to calculate and easy to apply in engineering.