A high-accuracy two-position alignment inertial navigation system for lunar rovers aided by a star sensor with a calibration and positioning function

Abstract

An integrated inertial/celestial navigation system (INS/CNS) has wide applicability in lunar rovers as it provides accurate and autonomous navigational information. Initialization is particularly vital for a INS. This paper proposes a two-position initialization method based on a standard Kalman filter. The difference between the computed star vector and the measured star vector is measured. With the aid of a star sensor and the two positions, the attitudinal and positional errors can be greatly reduced, and the biases of three gyros and accelerometers can also be estimated. The semi-physical simulation results show that the positional and attitudinal errors converge within 0.07″ and 0.1 m, respectively, when the given initial positional error is 1 km and the attitudinal error is 10°. These good results show that the proposed method can accomplish alignment, positioning and calibration functions simultaneously. Thus the proposed two-position initialization method has the potential for application in lunar rover navigation.

Analytic coarse alignment and calibration for inertial navigation system on swaying base assisted by star sensor

“…The position is also an important initial information for the inertial navigation process. Position estimation can be given based on (12). By reorganising (12) and ignoring b a , we have…”

“…Besides, the existing calibration, alignment and positioning of the integration of INS and star sensor at deep level data fusion is achieved through complex Kalman filter calculations. In [12], with the aid of a star sensor and the two positions on the ground, the attitude and position errors can be greatly reduced by Kalman filtering, and the biases of three gyros and accelerometers can also be estimated. In [13], an integration algorithm based on the star vector observations is derived considering the star sensor installation error.…”

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

“…At present, the research about north-seeker includes basic inertial navigation error model [ 1 , 2 ], observability analysis [ 3 , 4 ], in-drilling alignment with general dynamic error model [ 5 ], alignment algorithms based on gyro-compassing mode [ 6 , 7 ], alignment based on the interacting multiple model and the Huber methods [ 8 ], rapid fine alignment under marine mooring condition [ 9 ], alignment for SINS (strapdown inertial navigation system) in vehicular environment [ 10 ], alignment based on Riccati Equation and EM (expectation-maximization) convergence [ 11 ], alignment based on adjustment on separate-bias Kalman filter [ 12 ], application of nonlinear filtering in alignment [ 13 ], initial attitude estimation of tactical grade inertial measurement [ 14 ], alignment with robust adaptive unscented Kalman filter [ 15 ], alignment based on a group of double direct spatial isometries [ 16 ], alignment with state-dependent extended Kalman filter [ 17 ], application of redundant technology in north-finding [ 18 ], two-position algorithm [ 19 , 20 ], multi-position algorithm [ 21 , 22 ], rotary-modulation algorithm [ 23 , 24 , 25 ], nonlinear filter model for large misalignment angle [ 26 ], transfer north-seeking algorithm [ 27 , 28 , 29 ], transfer alignment based on cubature Kalman filter (CKF) method [ 30 ], north-finding based on the neural network technology [ 31 , 32 ], transfer algorithm based on sensors network and estimation of wing flexure deformation [ 33 ], fast stationary initial alignment based on extended measurement information [ 34 ], accurate fine alignment based on adaptive extended Kalman filters [ 35 ], compact north-seeker technology [ 36 ], north-seeking based on the information fusion technology [ ...…”

“…Hence, the structure and fabrication of SINS is complicated, and its cost is high. The literature [ 19 ] proposed a high accuracy two-position alignment for lunar rovers. Ref.…”

“…Especially, the performance of multi-position alignment algorithm is better than two-position alignment algorithm. However, such algorithms all require a precise rotating mechanism, some need the assistance of star sensor [ 19 ], and some are only suitable for static base [ 20 ]. Therefore, these algorithms are not suitable for the portable shipborne north-finder.…”