ID 原文 译文
11674 研究了间歇采样转发干扰对调频斜率极性捷变合成孔径雷达(chirp rate polarity jittered-synthetic aperture radar,CRPJ-SAR)的干扰效果。 Intermittent forward sampling disturbance to the frequency modulation slope is studied in polarity agility synthetic aperture radar (chirp rate polarity jittered - synthetic aperture radar, CRPJ - SAR) interference effect.
11675 建立了CRPJ-SAR间歇采样转发干扰信号模型,通过推导得到干扰的成像输出以及假目标位置和幅度的解析计算公式。 ‭Established CRPJ - SAR intermittent forward sampling disturbance signal model, was derived through interference imaging output and false target location and amplitude of analytic calculation formula.
11676 结果表明间歇采样转发干扰不仅能够在距离向形成等间隔分布的多阶假目标,而且当假目标阶数不为零时,在同一距离向存在3个沿方位向等间隔分布的假目标。 ‭Results show that the intermittent sampling forward interference can not only in the distance to the interval distribution of multistage false target, and when the false target order number is not equal to zero, at the same distance to the existing three along azimuth interval distribution of false target.
11677 仿真验证了理论分析的正确性。 ‭The simulation verifies the correctness of the theoretical analysis. ‭
11678 针对存在基站误差的目标无源定位问题,提出了一种基于修正牛顿算法的时差定位技术。 For base station error target passive location problems, this paper proposes a time positioning technology based on modified Newton algorithm.
11679 众所周知,牛顿法对初值要求较高,较差初值会导致迭代发散,而且基站位置误差也会导致牛顿算法Hessian矩阵维数扩大和目标函数的缓慢下降,使运算量变大。 It is well known that Newton's method for initial demand is higher, low initial value can lead to iteration divergence, and the position error and the base station can also lead to Newton algorithm Hessian matrix dimension expansion and slow decline of the objective function, the computational complexity.
11680 该算法利用最大似然方法确定目标函数,运用牛顿法对目标位置进行迭代求解. The algorithm is maximum likelihood method is used to determine the objective function, USES the law of Newton iteration solving of target location.
11681 对于计算过程中可能出现的病态Hessian矩阵,引入正则化理论修正病态的Hessian矩阵,使保证迭代收敛,同时简化算法降低Hessian矩阵的维数并且加速目标函数的下降趋势,使目标位置解脱离局部最小值,算法能够稳健高效的运行。 For possible pathological Hessian matrix in the calculation process, the introduction of regularization theory correct morbid Hessian matrix, thus ensure the iterative convergence, at the same time, simplify the algorithm to reduce the dimension of the Hessian matrix and accelerate the decline of the objective function, the target location solution out of the local minimum algorithm is robust and efficient operation.
11682 实验结果表明:相对于传统牛顿法,此算法在初始值的选取上具有稳健性,对误差选取较大的初始值,仍能够保证算法的收敛性,同时加速了收敛速度,降低了计算量; The experimental results show that: compared with the traditional Newton's method, this algorithm has robustness on the selection of initial values, select larger initial value of error, still can guarantee the convergence of the algorithm, and accelerate the convergence speed, reduces the amount of calculation;
11683 相对于现有闭合式定位方法,此算法在噪声较大时具有较好的定位精度。 Compared with the existing closed type locating methods, the algorithm has good localization accuracy when noise is larger.