ID 原文 译文
10134 针对现有高度估计算法缺乏对实际多雷达组网系统信息处理流程的考虑致使算法难以在实际系统中运用的问题,在岸海空协同预警体系下,从信息处理流程和信息处理方法两个方面,在探测单元信息缺维情况下,研究目标高度补偿问题。 In view of the existing lack of actual radar networking system more height estimation algorithm considering the algorithm of information processing is difficult to use in the actual system, onshore air and collaborative early warning system, from two aspects of information processing and information processing method, the detection unit information lack of dimensional case, the target altitude compensation problem.
10135 仿真结果表明,本文给出的算法可实现目标状态的无偏、稳定、精确估计,最终得到的目标海拔高度补偿估计结果可满足目标属性判别和威胁判断的需要。 ‭Simulation results show that the algorithm presented in this paper can achieve target state unbiased, stable and accurate estimation of the resulting target altitude compensation estimation results can meet the needs of the target attribute discriminant and threat judgment.
10136 另外仿真验证表明,本文算法对目标巡航高度具有很强的适应性,可用于解决处于岸海空探测单元共视范围内任意高度飞行目标的高度补偿问题。 ‭And simulation show that the algorithm of target cruising altitude has strong adaptability, can be used to solve in the shore air and detection unit were apparent within the scope of any altitude target altitude compensation problem. ‭
10137 针对无迹卡尔曼滤波(unscented Kalman filter,UKF)中自由调节参数的选取问题,通过研究不同的对于滤波性能的影响,提出基于量测一步预测信息的在线自调整的UKF方法。 For no trace Kalman filtering (unscented Kalman filter, UKF) free to adjust parameter selection problem, by studying the different effect on the filter performance, based on measured step prediction information online self-tuning UKF method.
10138 所提方法是通过根据每一滤波时刻量测的一步预测信息,对滤波参数进行选取,选出每一滤波时刻的最优滤波参数,从而实现算法的在线调整。 Proposed method is through according to each step prediction information filtering time measurement, the parameters selected for filtering, select the optimal filtering parameters of each filter time, so as to realize on-line adjustment of the algorithm.
10139 数值仿真表明,基于量测一步预测信息的自调整UKF对于真实状态的跟踪效果要优于固定参数的无迹卡尔曼滤波。 Numerical simulation shows that based on information of the measured step prediction self-tuning UKF for true state tracking performance is superior to the fixed parameters of no trace of kalman filtering.
10140 针对均匀线阵,利用信号的恒模特性,与容积卡尔曼滤波相结合,提出一种新的盲自适应波束形成算法。 For uniform linear array, using the signal of constant model, combined with a volume of kalman filter, put forward a new blind adaptive beamforming algorithm.
10141 通过对恒模算法的优化代价函数进行变换,使其满足系统状态空间模型。 ‭Through optimizing the cost function of constant modulus algorithm transform, make it meet the system state space model.
10142 利用容积卡尔曼滤波算法进行自适应滤波,以实现抑制干扰和消除噪声。 ‭Using volume kalman filtering algorithm for adaptive filter, in order to realize the suppression disturbance and eliminate noise.
10143 所提算法对状态空间模型中的系统噪声和过程噪声进行了自适应处理,免除滤波噪声参数的设置,增强了算法的通用性,并引入了收敛因子,加速系统的收敛速度。 ‭Proposed algorithm on the state space model of system and process noise in the adaptive processing, exempt from filtering noise parameter setting, enhance the versatility of the algorithm, and introduced the convergence factor, to accelerate the convergence speed of the system.