Krylov subspace method based on data preprocessing technology

The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest （SOI） component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom （DOF） of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.     

General and efficient parallel approach of finite element-boundary integral-multilevel fast multipole algorithm

A general and efficient parallel approach is proposed for the first time to parallelize the hybrid finiteelement-boundary-integral-multi-level fast multipole algorithm （FE-BI-MLFMA）. Among many algorithms of FE-BI-MLFMA, the decomposition algorithm （DA） is chosen as a basis for the parallelization of FE-BI-MLFMA because of its distinct numerical characteristics suitable for parallelization. On the basis of the DA, the parallelization of FE-BI-MLFMA is carried out by employing the parallelized multi-frontal method for the matrix from the finiteelement method and the parallelized MLFMA for the matrix from the boundary integral method respectively. The programming and numerical experiments of the proposed parallel approach are carried out in the high perfor- mance computing platform CEMS-Liuhui. Numerical experiments demonstrate that FE-BI-MLFMA is efficiently parallelized and its computational capacity is greatly improved without losing accuracy, efficiency, and generality.     

Multiple targets vector miss distance measurement accuracy based on 2-D assignment algorithms

An extension of 2-D assignment approach is proposed for measurement-to-target association for improving multiple targets vector miss distance measurement accuracy. When the multiple targets move so closely, the measurements can not be fully resolved due to finite resolution. The proposed method adopts an auction algorithm to compute the feasible measurement-to-target assignment with unresolved measurements for solving this 2-D assignment problem. Computer simulation results demonstrate the effectiveness and feasibility of this method.     

二阶自适应IIR陷波器复数算法的收敛性分析

针对背景噪声中复正弦信号的检测和估计问题,分析了自适应格型IIR陷波器的两种复数算法即梯度算法和简化的梯度算法的收敛性能,证明了输入白噪声方差的大小不影响收敛速度。并分析了由一阶自回归模型产生的有色噪声对算法的收敛性能的影响,为有色噪声中复正弦信号检测的自适应过程的参数的选取提供了理论依据。     

Simulated annealing algorithm for the optimal translation sequence of the jth agent in rough communication

In rough communication, because each agent has a different language and cannot provide precise communication to each other, the concept translated among multi-agents will loss some information and this results in a less or rougher concept. With different translation sequences, the problem of information loss is varied. To get the translation sequence, in which the jth agent taking part in rough communication gets maximum information, a simulated annealing algorithm is used. Analysis and simulation of this algorithm demonstrate its effectiveness.     

基于神经网络和PSO的机器人路径规划研究

提出一种神经网络和粒子群算法相结合的移动机器人路径规划方法。采用小波网络和RBF网络相结合的四层神经网络结构,克服了传统神经网络方法进行路径规划时对每个障碍均设计一些特定的隐节点,当障碍较多且环境动态时,网络结构庞大且神经元的阈值随时间的变化而需要不断改变的缺点。利用粒子群对神经网络的参数进行训练,在规定的代数内对网络参数优化,使得机器人在移动过程中能够快速响应环境的变化。通过对移动机器人在动、静态不同环境下的仿真实验,证明了方法的有效性。     

基于多元共生遗传神经网络的藻类预测仿真

提出一种多元共生遗传神经网络(PGANN),从遗传算法和神经网络——进化和学习两方面及其相互关系着手完善进化过程,以提高优化效果和泛化能力。该算法包括一个共生平衡交叉算子,一种多元选择策略和一种神经网络的分级优化策略,其中共生平衡算子能够兼顾进化过程中的方向性、多样性和自适应性;多元选择策略能够适应进化过程不同时段对选择压力不同的需求;而分级优化使运算规模和运算速度之间的矛盾得到缓解。将该改进的遗传神经网络PGANN应用于水库和湖泊有毒的优势蓝绿藻爆发预测,取得了满意的效果。     

三维粒子测速系统中遗传匹配法的设计

提出了基于遗传算法的三维粒子图像匹配方法及实际中的优化设计。该方法以视差为编码、结合SSD(sum of square difference)与SAD(sum of absolute difference)法对结果进行评估,并通过单点、多点的双交叉、变异与序列的混沌化处理达到对分析空间的搜索;最后,局部处理结合全局唯一性迭代检测进一步增加了匹配结果的可信度。利用优化前后所提算法对空间粒子图进行了对比及误差分析。结果表明:所提算法适用于粒子测速系统的立体匹配,能够给出较准确的视差信息。     

基于动态贝叶斯网的时变样本空间无监督学习

提出了针对概率分布参数时变的样本空间的参数估计问题,由于一般的聚类和EM算法等参数估计方法是针对概率分布时不变的样本空间的问题所采用的。因此,对于概率分布时变的样本空间而言,这些方法均不能有效、准确地估计实时变化的样本参数。通过构建动态贝叶斯网,利用先验和后验的知识进行预测和滤波,结合贝叶斯增量式学习方法并充分利用了其学习所获得的样本空间概率模型变化演进的规律,这样可以较准确、平滑地学习实时概率模型及其分布参数。     

空投设备缓冲气囊的优化设计

利用LS-DYNA对缓冲空投设备的气囊进行仿真计算,模拟其缓冲过程。同时利用遗传算法对气囊的排气孔面积、初始充气压力和排气压力进行优化。优化后的结果对空投实验具有很好的指导意义,可以减少气囊设计实验次数从而降低实验的成本。