搜狗做题网英语翻译卡尔曼滤波是一个“optimal recursive data processing algorithm(最优化
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英语翻译
卡尔曼滤波是一个“optimal recursive data processing algorithm(最优化自回归数据处理算法)’,它是根据上一状态的估计值和当前状态的观测值推出当前状态的估计值的滤波方法,是一种递推的过程.
首先引入一个离散控制过程的系统.该系统可用一个线性随机微分方程（Linear Stochastic Difference equation）来描述：
X(k)=A X(k-1)+B(U(k)+W(k)) （10）
再加上系统的测量值：
Y(k)=C X(k)+V(k) （11）
其中：X(k)——k时刻的系统状态,
U(k)——k时刻对系统的控制量.
A和B——系统参数,对于多模型系统,他们为矩阵.
Y(k)——k时刻的测量值,
C——测量系统的参数,对于多测量系统,C为矩阵.
W(k)和V(k)——过程和测量的噪声.他们被假设成高斯白噪声(White Gaussian Noise),他们的协方差（covariance ）分别是Q,R.
卡尔曼滤波是一个“optimal recursive data processing algorithm(最优化自回归数据处理算法)’,它是根据上一状态的估计值和当前状态的观测值推出当前状态的估计值的滤波方法,是一种递推的过程.
首先引入一个离散控制过程的系统.该系统可用一个线性随机微分方程（Linear Stochastic Difference equation）来描述：
X(k)=A X(k-1)+B(U(k)+W(k)) （10）
再加上系统的测量值：
Y(k)=C X(k)+V(k) （11）
其中：X(k)——k时刻的系统状态,
U(k)——k时刻对系统的控制量.
A和B——系统参数,对于多模型系统,他们为矩阵.
Y(k)——k时刻的测量值,
C——测量系统的参数,对于多测量系统,C为矩阵.
W(k)和V(k)——过程和测量的噪声.他们被假设成高斯白噪声(White Gaussian Noise),他们的协方差（covariance ）分别是Q,R.
Kalman filter is a "optimal recursive is done may regression data processing (optimization algorithm) ', it is according to the estimate of a state of the current state and the observations of the current state launched the estimate of filter method, is a recursive process.
At first introduces a discrete control process system. The system can be used a Linear Stochastic differential equation (Linear Stochastic heading gaap) on to describe:
X (k) = A X (k-1) + B (U (k) + W (k)) (10)
Plus the measured value of the system:
Y (k) = C X (k) + V (k) (11)
Among them: X (k)-time of the system status, k
U (k)-k for the control system of time.
A and B-system parameter, for many model system, they for the matrix.
Y (k)-k the measured value of the moment,
C-measuring the parameters of the system for measuring system, more, C for matrix.
W (k) and V (k)-process and measurement noise. They were hypothesis into White Gaussian White Gaussian Noise (home), their covariance (covariance) respectively is Q, R.
望采纳
At first introduces a discrete control process system. The system can be used a Linear Stochastic differential equation (Linear Stochastic heading gaap) on to describe:
X (k) = A X (k-1) + B (U (k) + W (k)) (10)
Plus the measured value of the system:
Y (k) = C X (k) + V (k) (11)
Among them: X (k)-time of the system status, k
U (k)-k for the control system of time.
A and B-system parameter, for many model system, they for the matrix.
Y (k)-k the measured value of the moment,
C-measuring the parameters of the system for measuring system, more, C for matrix.
W (k) and V (k)-process and measurement noise. They were hypothesis into White Gaussian White Gaussian Noise (home), their covariance (covariance) respectively is Q, R.
望采纳
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