Abstract
X-ray pulsar-based navigation (XPNAV) is one of the perfect ways for autonomous deep-space navigation in the future. Due to spacecraft state model errors and strong cosmic background noises, low navigation accuracy is one of the main problems in XPNAV. This paper proposes a robust navigation filtering method to reduce the serious effect of spacecraft state model errors and strong noises on XPNAV. This method uses state model errors and pulsar observation errors to estimate and correct the state model. And then, to predict the spacecraft’ state in the next moment with high precision, the gain matrix is adjusted in quasi real-time by using the fading factor to ensure a minimized state estimation error variance in the next moment and an orthogonal residual sequence at different times. Finally, experimental results of multi-group simulations show that the proposed method had significantly improved navigation accuracy. And the accuracy of the proposed method is better than that of $\text{H}\infty $ robust filter and STUKF, especially when the state model errors and noise are great. Under the same conditions, compared with the other two methods, the proposed method has the minimum navigation filtering error and the strongest robustness.
Highlights
Pulsars are rapidly rotating neutron stars that emit signals with a stable period [1]
Shen et al.: Robust Filtering Method for X-Ray Pulsar Navigation in the Situation of Strong Noises and Large State Model Errors the Jacobian matrix to be solved [18]–[20], which is helpful to improve the accuracy of X-ray pulsarbased navigation (XPNAV)
For improve the precision of XPNAV, based on the unscented Kalman filter (UKF), this paper proposes a nonlinear predictive strong tracking unscented Kalman filter (NPSTUKF) method by combining the advantages of the nonlinear prediction theory and strong tracking theory to reduce the serious effects of the state model errors and high levels of noises on orbit determination in XPNAV
Summary
Pulsars are rapidly rotating neutron stars that emit signals with a stable period [1]. L. Shen et al.: Robust Filtering Method for X-Ray Pulsar Navigation in the Situation of Strong Noises and Large State Model Errors the Jacobian matrix to be solved [18]–[20], which is helpful to improve the accuracy of XPNAV. A nonlinear predictive strong tracking unscented Kalman filter (NPSTUKF) is proposed to reduce the effect of state model errors and high levels of noises on the accuracy of the orbit determination in XPNAV. This method uses state model errors and observation errors to construct a criterion function of state model error.
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