A Robust Filtering Method for X-Ray Pulsar Navigation in the Situation of Strong Noises and Large State Model Errors

Abstract

X-ray pulsar-based navigation (XPNAV) is one of the perfect ways for autonomous deepspace 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 multigroup simulations show that the proposed method had significantly improved navigation accuracy. And the accuracy of the proposed method is better than that of H∞ 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.INDEX TERMS State model error, strong noise, X-ray pulsar-based navigation, robustness.

Comprehensive Measurement of Position and Velocity in the Transverse Direction Using the Crab Pulsar

“…Consequently, we utilize Equation ( 16) to determine the adjustment coefficient k for position and velocity, thereby synthesizing a reactive motion state measure that encapsulates both. To standardize the units and normalize the process, the positional error and velocity error are divided by their respective sampling intervals, as shown in Equation (18).…”

“…The estimation of a spacecraft's radial velocity using X-ray pulsars is achieved based on the Doppler effect, through shortterm observations of the pulsar period by the spacecraft [15,16]. In terms of navigation filtering, the methods include robust filtering algorithms for model uncertainties [17], the Augmented Sigma-point Kalman Filter algorithm for addressing pulsar direction errors [18], and the Unscented Kalman Filter algorithm based on multi-mode adaptive estimation [19]. Li N, Kang Z W, and others have combined Empirical Mode Decomposition (EMD) with an extended Kalman filter (EKF) to propose an adaptive extended Kalman filter method based on EMD [20].…”