基于Kriging近似函数的变复杂度近似模型

为解决飞行器多学科不确定性设计与优化过程中多次调用计算复杂的高精度学科分析模型带来的计算复杂性问题，结合变复杂度建模思想和Kriging近似函数，建立了基于Kriging近似函数的变复杂度近似模型。该近似模型的构造基于较少的高精度模型取样点和较多的低精度模型取样点，在满足近似精度的要求下可大大减小高精度模型近似模型构造的计算成本。数学算例和某飞行器气动力变复杂度近似模型算例证明了该方法的有效性。     

挠性航天器伸展动力学分析

本文导出了带挠性伸展附件航天器姿态动力学方程，研究了附件伸展和振动对姿态动力学的影响。对于附件按指数规律伸展情况，以及附件长度的次方随时间线性变化情况，给出了姿态角速率的渐近公式。     

基于原模图扩展的QC-LDPC构造方法

基于原模图构造的低密度奇偶校验码(LDPC)性能很大程度上取决于扩展算法。为此,提出了一种构造准循环低密度奇偶校验码(QC-LDPC)的新方法。所述算法经过两步扩展得到QC-LDPC:第一步是原模图去重边,在边置换条件的约束下,使扩展所得矩阵局部围长最大化;第二步进行准循环扩展,通过计算机搜索得到规定长度内的所有闭环路径,比较环长和近似环路外信息度得到置换矩阵的最优偏移量,目的是剔除连通性差的短环对码性能的负面影响。对于不存在重边的原模图,则直接进行准循环扩展。仿真结果表明,利用该方法构造的QC-LDPC在译码门限和误码平层两方面都具有优异的性能。     

A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets

The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs＇ minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood （DTSPN）. Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants （multi-UAV scenario and multiple groups of targets scenario）. The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.