非理想阵列的信号波达方向和多普勒频率估计

在均匀线阵存在误差的情况下 ,提出了一种信号波达方向和多普勒频率的估计方法。此方法在空间域 ,不仅考虑了阵元幅相误差 ,而且考虑了阵元位置误差 ,使包括幅相误差和位置误差在内的误差矩阵达到Frobe nius范数的最小值。在时间域考虑了测量噪声 ,使得多普勒频率估计的似然概率达到最大。仿真结果表明了此算法的有效性。     

基于动态预测企业生产计划模型

在静态的环境下 ,生产计划的决策主要来源于市场的需求预测 ,但是 ,如果在动态环境里 ,由于预测信息本身在不断地变化 ,预测值在不断地改变 ,如何将动态预测信息 (预测修正量 )转化成生产计划 ?研究需求信息向生产计划的动态转变与及时更新 ,具有很强的理论价值和重要的实际意义     

幅相误差对射频仿真系统目标位置精度的影响

依据电磁场理论，直接从三元组出发推导了射频仿真系统目标视在角位置方程。用数字仿真的手段，在整个阵面上随机均匀取点并模拟实际系统所产生的幅相误差，得出了几种情况下目标位置误差的统计分布，给出了它们的均值和方差。     

人工神经网络D最优设计的一种方法

利用初始的少数实例求初步的权值矩阵，根据该矩阵的人工神经网络模型找出偏导数向量的模最大时对应的设计矩阵，又根据该设计矩阵的实例求出新的权值矩阵，寻找新一轮设计矩阵。通过用泰勒级数展开的神经网络输出公式，求该输出函数的方差，证明了偏导数的向量的模最大是相关矩阵行列式最小(D最优)的充分条件。     

基于RBF的机械手建模误差补偿自适应控制

针对机械手动力学建模误差,提出了基于RBF神经网络误差补偿的自适应控制策略。在基于逆动力学的计算力矩控制方法的基础上,对系统输入与目标轨迹进行修正,设计了两种误差补偿自适应控制器。利用RBF神经网络对修正项在线自学习,并根据Lyapunov稳定性理论建立了网络权重自适应学习律,保证了跟踪误差的收敛及系统的稳定。以平面转动双臂机械手轨迹跟踪为例进行仿真,结果表明该方法能够有效地补偿建模误差,提高了系统的控制性能并使控制系统具有对参数摄动的鲁棒性,对于机械手自适应控制具有一定的可行性。     

Mobile channel estimation for MU-MIMO systems using KL expansion based extrapolation

In multi-user multiple input multiple output(MU-MIMO) systems,the outdated channel state information at the transmitter caused by channel time variation has been shown to greatly reduce the achievable ergodic sum capacity.A simple yet effective solution to this problem is presented by designing a channel extrapolator relying on Karhunen-Loeve(KL) expansion of timevarying channels.In this scheme,channel estimation is done at the base station(BS) rather than at the user terminal(UT),which thereby dispenses the channel parameters feedback from the UT to the BS.Moreover,the inherent channel correlation and the parsimonious parameterization properties of the KL expansion are respectively exploited to reduce the channel mismatch error and the computational complexity.Simulations show that the presented scheme outperforms conventional schemes in terms of both channel estimation mean square error(MSE) and ergodic capacity.     

Improved adaptive pruning algorithm for least squares support vector regression

As the solutions of the least squares support vector regression machine(LS-SVRM) are not sparse,it leads to slow prediction speed and limits its applications.The defects of the existing adaptive pruning algorithm for LS-SVRM are that the training speed is slow,and the generalization performance is not satisfactory,especially for large scale problems.Hence an improved algorithm is proposed.In order to accelerate the training speed,the pruned data point and fast leave-one-out error are employed to validate the temporary model obtained after decremental learning.The novel objective function in the termination condition which involves the whole constraints generated by all training data points and three pruning strategies are employed to improve the generalization performance.The effectiveness of the proposed algorithm is tested on six benchmark datasets.The sparse LS-SVRM model has a faster training speed and better generalization performance.     

Novel reference range selection method in InlSAR imaging

Aiming at the reference range selection for different antennas in interferometric inverse synthetic aperture radar (InISAR) systems, this paper proposes a respective focusing (RF) method. The reference ranges for echoes of different antennas are selected respectively for RF, which is different from the traditional uniform focusing (UF) with the same reference range applied to all the antennas. First, a comparison between UF and RF for InISAR signal model considering the ranging error is given. Compared with RF, UF has an advantage in overcoming the ranging error differences between different antennas. Then the influence of ranging error upon the interferometric imaging with RF is investigated particularly, and it is found that the ranging error differences between different antennas are far smaller than the wavelength, which is advantageous to imaging. By comparing the capabilities of interferometric imaging between RF and UF, it is concluded that RF is a better choice in conquering problems such as image mismatching and phase ambiguity even with ranging errors. Simulations demonstrate the validity of the proposed method.     

非均匀圆阵天线模型解模糊误差研究

基于干涉仪的立体基线法与传统测向解模糊方法相比,天线摆放方式更为灵活。针对这一优点,建立了非均匀圆阵天线阵列模型,给出了该模型理论测向误差的表达式。通过分析解算过程中影响测向误差的因素,提出了一种减小天线阵列实际测向误差的方法。为保证系统具有一定的解模糊能力,给出了该方法中天线孔径对系统解模糊频段的限制条件。基于建立的天线模型,通过Matlab仿真,验证了方法及理论分析的正确性,对合理选择天线盘孔径、减小测向系统的误差具有可行性。     

挠性捷联惯组的多位置对准技术

弹用惯组的误差是影响系统落点导航精度的关键因素。针对如何利用已知的航向信息最大程度地提高弹用挠性陀螺的实际使用精度的问题,将多位置对准技术应用于惯组误差的在线标定,利用行列式法对陀螺静态漂移误差进行了可观性分析,给出常用两位置对准的理论分析及最优三位置的限定方程。仿真结果表明,最优三位置对准可以有效分离常值漂移和与比力一次项有关的漂移,实现挠性陀螺静态漂移误差状态变量全部可观测,最大程度地降低其对系统误差的影响。