ID 原文 译文
26365 针对不确定性系统，提出一种非因果的鲁棒迭代学习控制方法。 In view of the uncertainty system, the paper puts forward a kind of causal method of robust iterative learning control.
26366 采用反馈控制和迭代学习控制分别设计并统一进行鲁棒性分析的方法，以提高系统的反馈性能和学习性能。 Using feedback control and iterative learning control design and unified method for robustness analysis, in order to improve the system of feedback and learning performance.
26367 首先采用H∞控制方法，保证反馈部分的鲁棒稳定性；其次采用二次型优化迭代学习控制律来提高系统的学习效率；最后结合μ分析理论，对迭代学习控制系统进行鲁棒性验证，并对优化学习律的参数进行修正。 First the h-infinity control method, is adopted to guarantee the robust stability of feedback part;The quadratic optimal iterative learning control law is used to improve the learning efficiency of system;Finally mu analysis theory, the robust iterative learning control system verification, and learn to optimize the parameters of the law.
26368 该方法吸取了非因果迭代学习律学习性能好的优点，也充分利用μ分析保守性小的特点来提高系统的性能。 The method learn the iterative learning law of cause and effect performance good advantages, also take full advantage of the characteristics of conservative small mu analysis to improve the performance of the system.
26369 算例结果验证了方法的有效性。 Numerical example results show the effectiveness of the method.
26370 相参积累可以提高小雷达散射截面(radar cross section，RCS)目标的信噪比，但由于目标的高速高机动性，长时间相参积累会导致回波的越距离单元走动和越多普勒单元走动问题。 Coherent accumulation can improve the radar scattering cross section (radar cross section, the RCS) of target signal to noise ratio, but due to the high speed high mobility goals, coherent accumulation for a long time can cause echo doppler unit to walking distance unit and more and more about the problem.
26371 首先建立了高机动小RCS目标的回波信号模型，并分析了目标回波信号的二维频域特性，在此基础上，提出了一种长时间相参积累检测新方法。 High mobility was established by the small RCS of target echo signal model, and analyzes the target echo signal of two-dimensional frequency domain feature, on this basis, this paper proposes a method to detect new coherent accumulation for a long time.
26372 基本思想是在距离频域-方位时域利用Keystone变换，校正由一次相位引起的越距离单元走动问题，然后乘以二次相位补偿函数解决越多普勒单元走动问题，通过构造目标函数对补偿函数中的加速度进行搜索，获得的目标函数的最大值即为相参积累值。 The basic idea is from the frequency domain - bearing time domain using the Keystone transform, caused by a phase correction is walking distance unit problem, and then multiplied by the quadratic phase compensation function solve the problem of the doppler unit moving, by constructing the objective function is applied to the acceleration compensation function to search, to obtain the objective function of the maximum value for the coherent accumulation.
26373 仿真结果验证了该算法的有效性。 The simulation results verify the effectiveness of the algorithm.
26374 针对传统粒子概率假设密度(probability hypothesis density，PHD)滤波跟踪被动多目标时，估计精度不高，且存在粒子退化，容易导致滤波器发散的问题，提出一种新的被动多目标跟踪算法———高斯厄米特粒子PHD滤波算法。 For traditional particle probability hypothesis density (aim-listed probability content density, PHD) filter passive tracking multiple targets, the estimation precision is not high, and there is particle degeneration, easily lead to filter divergence problem, put forward a new passive multiple target tracking algorithm - gaussian Hermitian particle PHD filter algorithm.