Celestial Navigation System (CNS) has characteristics of accurate orientation and strong autonomy and has been widely used in Hypersonic Vehicle. Since the CNS location and orientation mainly depend upon the inertial reference that contains errors caused by gyro drifts and other error factors, traditional Strap-down Inertial Navigation System (SINS)/CNS positioning algorithm setting the position error between SINS and CNS as measurement is not effective. The model of altitude azimuth, platform error angles, and horizontal position is designed, and the SINS/CNS tightly integrated algorithm is designed, in which CNS altitude azimuth is set as measurement information. GPF (Gaussian particle filter) is introduced to solve the problem of nonlinear filtering. The results of simulation show that the precision of SINS/CNS algorithm which reaches 130 m using three stars is improved effectively.
Hypersonic Vehicle (HV) which refers to a vehicle flying at Mach 5 or above has already been the research focus in aeronautic and aerospace fields with its great strategic military application values [1, 2].
Although Hypersonic Vehicle has many advantages, such as large flight envelope, high maneuverability, and well penetrability, the dynamic model of an HV is fast time varying and highly nonlinear because of its Mach numbers . Large-scale variations of altitude and velocity lead to uncertainties in the aerodynamic parameters [4, 5]. As a result, HV is a highly nonlinear and uncertain system. Consequently, it is difficult to measure or estimate the dynamic state and characteristics of the vehicle . Autonomous navigation system with high accuracy and reliability has been a major constraint on the improvement in performance of HV.
In recent years, owing to the development of microelectronics and computer technology, as well as the accuracy improvement of Charge Coupled Device (CCD), Charge Inject Device (CID) star trackers, and inertial components, the Strap-down Inertial Navigation System (SINS) and Celestial Navigation System (CNS) are widely used in in aircrafts .
The celestial navigation method is a kind of autonomous navigation technology which can determine the vehicle’s position and attitude . Since Celestial Navigation System (CNS) has characteristics of accurate orientation and strong autonomy, it has become an important component of integrated navigation system of HV .
The conventional celestial navigation utilizes the inertial navigation platform technology to realize the vertical vector and compute the vehicle’s navigation information by measuring the relative position changes between the vertical vector and the celestial vector. The navigation accuracy of this method depends largely on the accuracy of horizontal reference and celestial sensor measurements . CNS usually gets the inertial horizon reference by inertial navigation platform. Considering that the strap-down type replacing the platform type has been the development trend of INS, it has become extremely difficult to improve the accuracy of the inertial horizon references due to the impact of INS core instruments (gyros and accelerometers) error .
In traditional SINS/CNS integrated mode, CNS utilizes the position and attitude information of INS to calculate celestial positions and heading attitude and then realize periodic correction of the INS drifts. This mode can damp the divergence of INS position errors; however, since the CNS location and orientation mainly depend upon the inertial reference that contains errors caused by gyro drifts and other error factors, this postcorrection method is not effective.
A number of classical approaches, the Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filtering (PF), and so forth, have been proposed to the information fusion. Kalman filtering is commonly used algorithms for information integration. A prerequisite for using Kalman filter is that the system dynamics and noise statistics are known . But considering the HV is a highly nonlinear system, the dynamic characteristics of the HV and external environment make these premise conditions often not met.
PF can effectively solve the problem of nonlinear filtering [13, 14]. However, its limitation is obvious.
(1) PF occasionally has the particle impoverishment (PI) problem that results from resampling process .
(2) The number of particles will increase at a rapid rate along with the increase of the system dimensions.
The Gaussian PF avoids the PI problem that is the disadvantageous feature of PF in the estimation of a static parameter. Furthermore, resampling process is not required in the GPF algorithm. Therefore, its computational complexity (CC) is significantly reduced compared to particle filtering.
This paper will carry out the research on the SINS/CNS integrated navigation algorithm for the HV. In order to improve the accuracy and reliability of SINS/CNS integrated systems, the scheme and algorithm of airborne SINS/CNS integrated navigation based on celestial angle observation have been presented. The theory of SINS/CNS integrated navigation system based on celestial altitude angle observation information has been discussed adequately; a model with celestial altitude angle, platform error angles, and horizontal position is deduced. Meanwhile, a new SINS/CNS tightly integrated localization algorithm using Gaussian particle filter (GPF) is presented, which makes full use of SINS and CNS navigation information to achieve higher accuracy of the SINS/CNS integration.