在随机线性不确定系统中，设计一个满意的状态估计器在工程中有很大的实际意义。
In the Linear stochastic systems with uncertainties, designing a satisfactory State-estimation is of practical significance in the field of engineering.
讨论的问题包括线性调节器，状态估计器，状态线性函数观测器，及不完全或有噪声测量的线性二次控制。
The discussion includes linear regulator, state estimator, observer for a linear functional of state, and linear quadratic control with incomplete or noisy measurements.
提出了一类基于神经元状态估计器的自适应广义极点配置控制，研究了该控制系统的网络结构和权值学习方法。
This paper presents a class adaptive pole assignment control of servo systems based on neural state estimation and develops the system structure and the weight learning algorithms.
针对噪声不确定性，文章采用对策论的基本原理，导出了一种最小化不确定下最坏性能的极小极大鲁棒状态估计器。
Using game theory, the minimax robust state estimator is designed, which can minimize the worst performance un-der the uncertain noise.
本文提出一类不依赖被估计系统模型微分状态估计器，参数少、精度高，通过分析其根轨迹和极点要求配置合适的参数；
This paper presents a new differential state estimator, which does not rely on the model of the estimated system and has higher accuracy with a few parameters.
论述了带反馈分布式信息融合系统中传感器观测维数不同时的状态估计方法。
The method of state estimation is discussed, when radars have different observation dimension in one distributed data fusion system with feedback.
基于一定的解码状态，声码器通过最小均方误差（MMSE）估计的方法估计最优参数，充分降低信道误码对重建语音质量的影响。
The minimum mean square error (MMSE) is computed for each decoding state to estimate optimal parameters and to reduce the influence of the bit error.
基于线性无偏最小方差估计理论，提出了一种任意相关噪声异类传感器非线性系统状态矢量融合算法。
Based on linear unbiased minimum variance estimation theory, a fusion algorithm which fused the state vector of nonlinear systems with dissimilar sensors with arbitrary correlated noises is developed.
用合适的状态空间邻近矢量进行非线性局部分析，即使没有基音周期估计，短时预测器同样能建立长时相关性模型。
With an appropriate state space neighbour for the nonlinear local analysis, the short_delay predictor is also able to effectively model the long_term correlation without pitch estimation.
采用数字寻优方法确定观测器校正矩阵参数，从而实现了活性污泥过程重要状态变量的精确在线估计。
Therefore, the accurate estimation of important state variables has been implement on-line for the activated sludge processes.
基于多传感器多模型信息，给出了目标状态基于全局信息融合估计的一种新算法，并通过计算机仿真验证了这种算法的有效性。
Based on Multi_sensor Multi_model information, we present a new algorithm based on total information fusion estimation on target state. We prove the validity of this algorithm by computer.
逐次正交化分布式卡尔曼滤波器是对大系统进行状态估计的一种新方法。
The Successive Orthogonalization Decentralized Kalman Filter (SODKF ) is a new method which is used for large system state estimation.
设计扩展卡尔曼滤波器进行卫星编队轨道状态估计，数学仿真结果验证了这种导航方案和算法的有效性。
The orbit states estimation is achieved through the extended Kalman filters design. The simulation results verify the validity of this navigation method, and show preferable navigation accuracy.
提出了利用多传感器数据融合的方法估计无人机的状态参数。
The method of utilizing multisensor data fusion to estimate UAV state parameters is proposed.
为减轻预测器的发散性，对初始状态进行估计。
To alleviate divergence of the predictor, initial states is estimated.
研究了广义离散随机线性系统的多传感器信息融合状态估计问题。
Multi-sensor information fusion state estimation problem for descriptor discrete-time stochastic linear systems is studied.
采用卡尔曼滤波器对目标进行跟踪时，目标初始状态估计是影响初始阶段跟踪精度的一个重要原因。
When using Kalman Filter to track a target, estimation of the initial state of the target is an important factor influencing tracking precision in the initial phase.
然后采用强跟踪滤波器估计过程状态及传感器偏差，传感器偏差估计用于驱动一个故障检测逻辑。
Then, the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic.
考虑了广义离散随机线性系统的多传感器信息融合状态估计问题。
The problem of multi-sensor information fusion state estimation for descriptor discrete-time stochastic linear systems is considered.
给出了网络控制系统在不完全状态信息时系统状态的线性最优估计器；
The optimal estimator of system state for networked control systems without full state information is presented.
考虑一类时延网络控制系统，提出了一种具有时延补偿功能的卡尔曼滤波器设计方法，并对系统进行状态估计。
Considering a class of networked control systems with time delays, a novel method was proposed to design Kalman filters with delay compensation, and it was used to estimate the state of the system.
估计状态通过引入高增益观测器得到，实现了系统的输出反馈控制。
A high gain observer is employed to obtain the estimation of states and then output feedback controller is constructed.
通过引入成型滤波器，采用EKF，提高了状态估计的精度，实现对随机海浪扰动力和力矩的估计。
By using the forming filter and EKF, the precision of states estimation is increased and a effective estimation of stochastic sea interference is performed.
将TVAR模型的信号和反射系数矢量增广为状态矢量后，应用高斯粒子滤波器(GPF)估计TVAR的模型参数，构造了语音增强算法。
When TVAR model signal and reflection coefficients were extended to state vector, Gaussian Particle Filter (GPF) was applied to estimate parameters of TVAR model.
并将该模型用于建立一个在DRS观测值存在的情况下，状态向量估计的非线性平滑器。
This model is used to bui1d a nonlinear smoother for the estimation of the state vector when DRS measurements are available.
将健康参数作为增广的状态变量，设计了卡尔曼滤波器，从而可以根据可测参数的偏离量估计得到健康参数。
By taking health parameters as augmented state variables, a Kalman filter was then designed to predict the health parameters from the deviation of measurable parameters.
然后，在系统状态不完全可测的情况下，通过设计高增益观测器对系统的状态进行估计，实现输出反馈控制器设计。
Then we design the output feedback controller by introducing the estimator of the states for the case where system states are unknown.
本文提出适合于两级混合式多传感器系统的全局最优状态估计解。
This paper presents a globally optimal composite filtering solution for a two-level hybrid multisensor system.
该降维观测器能保证估计状态以指数规律渐近趋近系统的真实状态，并且通过观测器增益参数的适当选取，可使状态估计误差以指定的收敛速度趋于零。
By selecting the gain of the observer, the observer can guarantee the estimated states converge exponentially to the true states of the system with arbitrary rate of convergence.
该降维观测器能保证估计状态以指数规律渐近趋近系统的真实状态，并且通过观测器增益参数的适当选取，可使状态估计误差以指定的收敛速度趋于零。
By selecting the gain of the observer, the observer can guarantee the estimated states converge exponentially to the true states of the system with arbitrary rate of convergence.
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