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    A New Algorithm of Steady-state Kalman Filter Gain
    稳态Kalman滤波器增益的一种新算法
    NEW ALGORITHMS OF STEAD STATE KALMAN FILTER GAIN
    稳态Kalman滤波器增益新算法
    A Simple Calculation Method for the Gain of Universal Two Order Filter on OTA's
    一种计算OTA通用滤波器增益的简便方法
    Using the modern time series analysis method, based on the controlled autoregressive moving average(CARMA) innovation model, two new algorithms of steady state Kalman filter gain for stochastic control systems are presented, where the solution of the Riccati equation is avoided.
    应用现代时间序列分析方法,基于受控的自回归滑动平均(CARMA)新息模型,提出了随机控制系统稳态Kalman滤波器增益的两种新算法,避免了求解Riccati方程.
    The correlations between measurement noises and processing noises are added to the gain matrix of tracking filter, so information that can describe multi-sensors fusion systems is increased.
    在跟踪滤波器的增益阵中引入测量噪声与过程噪声的相关量和测量噪声之间的相关量,增加了描述多传感器融合系统的信息量.
    In the part of self-checkout, PI(Proportional Integral) control arithmetic is put forward to check the gain K and the calculated results show the method's effectiveness.
    在自校验部分提出了将PI(比例、积分)算法用于数字滤波器增益K值的确定,取得了不错的效果;
    Unfortunately, due to the finite gain-bandwidth product, GIC derived filters suffer from severe Q-enhancement for gains more than 2. Further inveestigations show that the finite gain-bandwidth product of the operational amplifier causes a nagetive conductance to appear.
    但是,当滤波器的增益大于2时,“GIC”导出式滤波器由于运算放大器的有限增益带宽乘积,有Q增大的缺点。
    The gain, frequency and Q of these filters can be controlled, because the value of the transconductance element is controlled with a voltage.
    由于单跨导元件的跨导值可由外加电压控制,因此滤波器的增益、频率和Q值也受控制。
    A new adaptive Kalman filter is presented for the single output system with unknown noise statistics. By a time series analysis, a new and simpler estimation algorithm for the gain of the steady-state optimal Kalman filter is given.
    对于带未知噪声统计的单输出系统,本文提出了一种新的自适应Kalman滤波器,应用现代时间序列分析方法,基于ARMA新息模型的滑动平均(MA)参数的在线辨识,提出了稳态最优Kalman滤波器增益估计的一种新算法,比Mehra的算法简单。
    Using the modern time series analysis, based on the ARMA innovation model. this paper presents a new algorithm of steady-state Kalman filter gain.
    用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增益阵的一种新算法。
    By using the modern time series analysis method and based on the ARMA innovation model,two new algorithms of steady state Kalman filter gain are presented,and their equivalence is proved. The self tuning Kalman filters can be implemented by using a recursive identifier of parameters for the ARMA innovation model,in conjunction with the new algorithms. A simulation example shows usefulness of the proposed algorithms.
    应用现代时间序列分析方法,基于ARMA新息模型,提出了稳态Kalman滤波器增益的两种简单的新算法,并证明了它们的等价性.应用ARMA新息模型参数的递推辨识器伴随新算法,可实现自校正Kalman滤波器.仿真例子说明了其有效性.
    The altitude difference between a sextant reading and a prediction reading derived from the CMT and the estimated position (EP) mutiplied by factor K φ and K λ from the Kalman filter can generate the position difference between EP and the optimal estimated position,even if with only a single observation.
    六分仪观测值与依赖于GMT和推算船位的该值的预测值之差,与来自卡尔曼滤波器增益阵的KΦ和Kλ相乘,可以得到推算船位与最优估计船位的差值DΦ和Dλ。 它们与推算船位的线性组合可以实时地给出最佳船位,即使只有一次观测亦是这样。
 

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