Time filter

Source Type

Beijing, China

The China Academy of Space Technology is a Chinese space agency and subordinate of China Aerospace Science and Technology Corporation . It was founded on 20 February 1968 and is the main spacecraft development and production facility in China. On 24 April 1970 CAST successfully launched China's first artificial satellite Dong Fang Hong I. Wikipedia.

Wang F.,China Academy of Space Technology | Huang J.,Chinese University of Hong Kong | Zhao Y.,Peking University
IEEE Transactions on Wireless Communications | Year: 2012

Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to the dynamic resource availability induced by the licensed users' activities. In this paper, we derive the optimal admission control and channel allocation decisions in cognitive overlay networks to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largest-delay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve close-to-optimal performances with low complexities. © 2012 IEEE. Source

Li J.,China Academy of Space Technology | Fang J.,Beihang University
IEEE Transactions on Instrumentation and Measurement | Year: 2013

Inertial sensor errors include deterministic errors and stochastic errors. Deterministic errors can be calibrated in laboratory by simple computation technique. Stochastic errors can be determined during calibration by adopting special methods because of their random character. The simplest method to determine the stochastic errors for inertial sensors is the Allan variance. This kind of method needs large data to fully characterize the stochastic errors. The normal nonoverlapped Allan variance has quite poor estimation accuracy in long cluster time. The fully overlapping Allan variance and traditional total variance have better estimation accuracy in long cluster time but are quite time consuming for large data set. The not fully overlapping Allan variance and nonoverlapped total variance are suitable for large data set to improve the estimation accuracy in long cluster time with much less time, but their accuracy is still relatively poor in comparison with not fully overlapping total variance. Whereas the not fully overlapping total variance is relatively time consuming and, compared with Allan variance, there is a bias which is not easy to be corrected. This paper proposes a sliding average Allan variance that has comparable estimation accuracy with total variance. The data are not required to extend as the total variance; thus the calculation burden could be reduced greatly. Therefore, it is more suitable for large data set. In addition this method has no bias in comparison with Allan variance, which means no bias correction is required. This method is applied to 12-h static data of three gyroscopes from a position and orientation system with good performance. © 2013 IEEE. Source

Dou L.,Nanjing University of Science and Technology | Dou J.,China Academy of Space Technology
IEEE Transactions on Advanced Packaging | Year: 2010

This paper applies the LaxFriedrichs technique, usually used in fluid dynamics, to transmission line sensitivity analysis. The LaxFriedrichs difference scheme for sensitivity analysis of both uniform and nonuniform transmission lines is derived. Based on this scheme, a method for analyzing multiconductor transmission line sensitivity, which does not need to be decoupled, is presented by combining with matrix operations. Using numerical experiments, the proposed method is compared with the characteristic method and the fast Fourier transform approach. With the presented method, the sensitivity of a nonlinear circuit including nonuniform multiconductor transmission lines is analyzed and the results are verified by the HSPICE perturbation method. The proposed method can be applied to either linear or nonlinear circuits, which include lossy nonuniform multiconductor transmission lines, and is proved to be efficient. © 2006 IEEE. Source

Zhang G.,China Academy of Space Technology
Electronics Letters | Year: 2016

From Sidon sequences, two explicit methods for constructing (2J, 2L)-regular type-II quasi-cyclic (QC) low-density parity-check (LDPC) codes are proposed without four cycles. The advantage of the new methods is two-fold: (i) compared with the existing lower bound of circulant permutation matrix (CPM) sizes above which type-II QC-LDPC codes exist without four cycles, the novel constructions enable much tighter lower bounds; and (ii) the codes constructed by the first method perform almost as well as type-II QC-LDPC codes from perfect cyclic difference set, while possessing a much more flexible CPM sizes. © The Institution of Engineering and Technology 2016. Source

Gu Y.,Harbin Institute of Technology | Wang S.,Harbin Institute of Technology | Wang S.,China Academy of Space Technology | Jia X.,Australian Defence Force Academy
IEEE Transactions on Geoscience and Remote Sensing | Year: 2013

In this paper, we address a spectral unmixing problem for hyperspectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of spectral unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperspectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass spectral differences and improve unmixing accuracy, compared to the state-of-the-art algorithms tested. © 1980-2012 IEEE. Source

Discover hidden collaborations