Due to the high maneuverability of a hypersonic vehicle, the measurements for tightly coupled INS/GNSS (inertial navigation system/global navigation satellite system) integration system inevitably involve errors. The typical measurement errors include outliers in pseudorange observations and non-Gaussian noise distribution. This paper focuses on the nonlinear state estimation problem in hypersonic vehicle navigation. It presents a new innovation orthogonality-based robust unscented Kalman filter (IO-RUKF) to resist the disturbance of measurement errors on navigation performance. This IO-RUKF detects measurement errors by use of the hypothesis test theory. Subsequently, it introduces a defined robust factor to inflate the covariance of predicted measurement and further rescale the Kalman gain such that the measurements in error are less weighted to ensure the filtering robustness against measurement errors. The proposed IO-RUKF can not only correct the UKF sensitivity to measurement errors, but also avoids the loss of accuracy for state estimation in the absence of measurement errors. The efficacy and superiority of the proposed IO-RUKF have been verified through simulations and comparison analysis.

Introduction

Hypersonic vehicle refers to a vehicle at the speed of Mach 5 or above. Due to the merits such as large flight envelope, high maneuverability and speedy global reach, hypersonic vehicle has received great attention in the recent years in both aeronautic and astronautic fields for various civil and military applications [1], [2]. As the ‘‘eye’’ of a hypersonic vehicle, the navigation system is the primary element of the overall vehicle flight control system (navigation, guidance and control system). The position, speed and attitude information provided by the navigation system is directly related to the accuracy and reliability of the vehicle guidance and control loop [3]. Nowadays, the INS/GNSS (inertial navigation system/global navigation satellite system) integration has been a widely used navigation technique for hypersonic vehicles [4], [5]. The integration of INS and GNSS overcomes the limitations of both standalone systems, i.e., the growth of navigation errors with time for INS as well as the typical low update rate of GNSS measurements. Thus, it can provide a superior performance comparing to either INS or GNSS [6]–[۸]. The integration of INS and GNSS can be classified into two categories [9]–[۱۱]. One is the loosely coupled integration which employ the velocity and position estimations solved by GNSS to assist INS. This method is simple in principle and easy to implement. However, the number of observable GNSS satellites frequently drops to below four due to high maneuverability, leading to the poor stability and reliability.