A Coarse Alignment Based on the Sliding Fixed-Interval Least Squares Denoising Method

The observation vectors in traditional coarse alignment contain random noise caused by the errors of inertial instruments,which will slow down the convergence rate.To solve the above problem,a real-time noise reduction method,sliding fixed-interval least squares(SFI-LS),is devised to depress the noise in the observation vectors.In this paper,the least square method,improved by a sliding fixed-interval approach,is applied for the real-time noise reduction.In order to achieve a better-performed coarse alignment,the proposed method is utilized to de-noise the random noise in observation vectors.First,the principles of proposed SFI-LS algorithm and coarse alignment are devised.A simulation test and turntable experiment were executed to demonstrate the availability of the designed method.It is indicated that,from the results of the simulation and turntable tests,the designed algorithm can effectively reduce the random noise in observation vectors.Therefore,the proposed method can enhance the performance of coarse alignment availably.

Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems.When the truth-mode mismatch is encountered,existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable.We develop a fixedinterval smoothing method based on forward-and backward-filtering in the Variable Structure Multiple Model(VSMM)framework in this paper.We propose to use the Simplified Equivalent model Interacting Multiple Model(SEIMM)in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters,and design a re-filtering procedure in the model-switching stage to enhance the estimation performance.To improve the computational efficiency,we make the basic model-set adaptive by the Likely-Model Set(LMS)algorithm.It turns out that the smoothing performance is further improved by the LMS due to less competition among models.Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.

Square-root divided difference Rauch-Tung-Striebel smoother

A square-root version of the divided difference Rauch-Tung-Striebel (RTS) smoother is proposed in this paper. The square-root variant essentially propagates the square roots of the covariance matrices and can consistently improve the numerical stability because all the resulting covariance matrices are guaranteed to stay positive semi-definite. Furthermore, the square-root form ensures reliable implementation in an embedded system with fixed or limited precision although it is algebraically equivalent to the standard form. The new smoothing algorithm is tested in a challenging two-dimensional maneuvering target tracking problem with unknown and time-varying turn rate, and its performance is compared with that of other de-facto standard filters and smoothers. The simulation results indicate that the proposed RTS smoother markedly outperforms the associated filters and gives slightly smaller error than an unscented-based RTS smoother.

Efficient high-accuracy north-south station-keeping strategy for geostationary satellites

Station-keeping(SK) is indispensable in actual geostationary(GEO) satellite missions. Due to the luni-solar gravity perturbations, the inclination of a GEO satellite suffers the issues of secular drift and long-period oscillation. Current north-south(NS)SK strategies maintain the GEO satellite’s orbit with high accuracy but low fuel efficiency. In this work, an efficient highaccuracy NS-SK strategy is developed for the GEO satellites. First, an averaging method is employed to decrease the accumulation of the secular drift within a one-solar-day SK cycle, while the long-period oscillation caused by the solar gravity is damped to further improve the orbital accuracy using the impulse and finite-thrust propulsions. Second, we contribute a fueloptimal cycle that reduces the fuel consumption and a fixed-interval cycle that executes SK control in fixed time interval every day to further enhance the proposed NS-SK strategy. Numerical simulations show that the improved strategy can achieve highaccuracy NS-SK with little fuel consumption. Moreover, results also demonstrate that the fixed-interval cycle can reach higher NS-SK accuracy while consuming less fuel.