一种特殊白化滤波器的广义最小二乘法

 本文提出一种采用特殊白化滤波器的广义最小二乘法,简记为GLS（SF）。这种算法中不需要单独估计白化滤波器的阶次及参数,只需迭代估计模型参数,算法简单,准确度和收敛性也较好。对于输入噪声较小、输出端测量噪声近似白噪声的系统来说,文中证明,用此算法所得模型输出与系统输出观测值之误差平方和最小。文中给出几个实际动态校准中建立动态数学模型的例子。     

高保真气动伺服弹性系统降阶技术研究

采用高保真非定常气动力辨识技术获得的气动伺服弹性(ASE)系统时域模型往往阶次较高,不利于控制系统设计的应用.为了有效降低高阶ASE系统阶次,研究了高保真ASE模型的降阶特性,分别采用平衡截断、奇异摄动以及平衡奇异摄动模型降阶技术,结合BACT系统讨论了各种方法在ASE模型降阶中的应用情况,通过仿真比较得知,平衡奇异摄动法应用于ASE模型的降阶具有一定的优势,可以大幅降低模型阶次,且能够保证降阶模型的精度.     

基于迭代SVD—切向插值方法的MIMO系统降阶

为了进一步提高多输入多输出(MIMO)时不变系统模型降阶算法的精度和效率,结合奇异值分解(SVD)降阶和切向插值降阶方法的优点,提出了一种基于迭代SVD-切向插值模型降阶方法(ISTIA)。对于MIMO时不变系统模型降阶,其优点是计算简单有效、性能鲁棒。最后通过对标准算例和气动伺服弹性(ASE)模型的降阶仿真,证明了本文方法的准确性和有效性。     

广义奇异摄动降阶方法在控制器降阶中的应用

 首先推导了广义奇异摄动降阶和奇异摄动降阶的 Schur方法,解决了均衡变换可能出现病态的问题,并且算法具有较好的鲁棒性。然后,将其应用于控制器降阶,并同其它方法比较,认为原系统带宽附近的控制器降阶误差对降阶效果影响较大,而广义奇异摄动降阶法通过参数α的选择,能取得较好的降阶效果。可供工程设计参考。     

自适应滤波在GFSINS/GPS组合导航系统中的应用研究

以九加速度计的惯性测量单元为例,介绍了无陀螺捷联惯性导航系统(GFSINS)的工作原理;对GF-SINS/GPS组合导航系统选择滤波器问题进行了理论分析,提出了基于自适应滤波器的组合方法;该方法通过在线联合估计噪声统计特性和系统状态,有效地解决了GFSINS/GPS采用间接法组合时,无法得到准确系统噪声统计特性的问题.通过仿真验证了该方法的有效性.     

基于LMI的鲁棒滤波在无人机飞控系统中的应用

针对无人机在实际飞行过程中存在外界干扰以及传感器量测噪声的问题,应用线性矩阵不等式理论对无人机飞控系统进行了降阶鲁棒滤波器设计.讨论了适用于连续系统的降阶鲁棒滤波算法以及滤波器存在的条件.最后,进行了无人机纵向高度保持阶段的数字仿真,给出迎角与俯仰角速度的仿真曲线,仿真结果验证了该算法的合理性和有效性.     

武装直升机控制增稳系统的设计

用现代控制理论的方法，对武装直升机的控制增稳系统进行了设计。所给的状态方程为２９阶，其中包括了执行机构及旋翼动态、机体挠性动态的参数。采用内部平衡系统的性质来获得一个降阶模型，用特征结构配置的方法来设计一个控制器，从而提高现代攻击直升机的操纵品质。同时还比较了降阶前后系统的性能，用降阶的方法说明了旋翼动态、直升机挠性动态对系统的影响。设计结果证明，系统得到了良好的解耦效果及闭环特性。     

基于增量学习的非定常气动力参数化降阶模型

气动降阶模型（ROM）是预测非定常气动力的有效工具,具有高精度和低计算成本的优点,近年来许多研究证实了该方法的有效性。但是关于飞行参数变化时,ROM的鲁棒性还需要进一步提高。为了提高ROM对不同飞行参数下的气动力预测能力,提出了基于最小二乘支持向量回归（LS-SVR）和增量学习算法的参数化降阶模型。LS-SVR是一种具有良好泛化能力的回归方法,基于LS-SVR的增量学习算法的主要贡献是在增加新样本集时,不需要重新学习整个数据集。为说明该方法的有效性,基于两自由度NACA64A010翼型构建参数化非定常气动力降阶模型。为了训练气动力输入和相应输出之间的关系,将马赫数和迎角作为附加的模型输入。仿真结果表明,该降阶模型能够准确描述气动力和气动弹性系统在不同飞行参数下的动态特性。     

基于置信度加权的组合导航数据融合算法

 针对联邦滤波融合算法中由于模型量测噪声统计特性未能被准确描述导致其子滤波器误差变大，进而导致联邦滤波估计出现偏差的问题，为了改进联邦滤波融合方法，将模糊自适应卡尔曼滤波方法和置信度加权方法与联邦滤波融合方法相结合，应用于组合导航系统。该方法首先将模糊自适应卡尔曼滤波方法应用于各子滤波器，使其能够跟踪真实量测噪声统计特性。然后通过模糊方法计算得到各子滤波器的置信度，进而得到联邦滤波器的置信度，再由得到的置信度对各子滤波器及联邦滤波器输出进行加权，得到最终的全局输出。对车载组合导航系统的仿真结果表明，这种算法对量测噪声具有较强的自适应性，能够抑制置信度低的子滤波器在融合系统中所占的权重，提高联邦滤波融合算法的精度，是一种可行的车载组合导航数据融合算法。     

用最小二乘拟合残差统计观测数据的误差特性

本文介绍了用最小二乘曲线拟合残差来统计观测数据误差特性的方法。当观测误差时间序列不是白噪声而是相关噪声时,它不仅可以统计误差序列的方差,还可以统计相关系数。文章还介绍了通过统计假设检验确定拟合曲线多项式阶数的方法,并首次用于对实测数据的检验,得到了令人满意的结果。     

Reduced state estimator for systems with parametric inputs

A reduced state estimator is derived for systems with bounded parameters as inputs. Optimal filter gains are derived for minimizing the total covariance of the estimation error due to measurement noise and parameter uncertainty. It is shown that these filter gains for a two-state system with a Gaussian parameter satisfy the Kalata relation in steady state. Equations are also derived for optimally filtering measurements in arbitrary time order. This reduced state estimator offers novelties over a traditional Kalman filter in its application to the class of problems considered. The total error covariance, which is minimized, makes no use of plant noise. Furthermore, the filter is easier to optimize in high dimensional and multiple sensor applications as well as in processing out-of-sequence measurements.     

Discrete-time min-max tracking

The problem of optimal state estimation of linear discrete-time systems with measured outputs that are corrupted by additive white noise is addressed. Such estimation is often encountered in problems of target tracking where the target dynamics is driven by finite energy signals, whereas the measurement noise is approximated by white noise. The relevant cost function for such tracking problems is the expected value of the standard H/sub /spl infin// performance index, with respect to the measurement noise statistics. The estimator, serving as a tracking filter, tries to minimize the mean-square estimation error, and the exogenous disturbance, which may represent the target maneuvers, tries to maximize this error while being penalized for its energy. The solution, which is obtained by completing the cost function to squares, is shown to satisfy also the matrix version of the maximum principle. The solution is derived in terms of two coupled Riccati difference equations from which the filter gains are derived. In the case where an infinite penalty is imposed on the energy of the exogenous disturbance, the celebrated discrete-time Kalman filter is recovered. A local iterations scheme which is based on linear matrix inequalities is proposed to solve these equations. An illustrative example is given where the velocity of a maneuvering target has to be estimated utilizing noisy measurements of the target position.     

涡扇发动机变参数鲁棒H_∞滤波器设计

针对存在建模误差及测量噪声干扰条件下的涡扇发动机性能参数估计问题,标准卡尔曼滤波及其改进算法滤波估计误差收敛速度慢,滤波估计精度低,对不确定测量噪声及建模误差较为敏感,为此本文提出了一种变参数鲁棒H_∞滤波器设计方法。该方法采用仿射参数依赖Lyapunov函数设计满足H_∞性能指标要求的鲁棒滤波器,通过引入凸多胞技术,将参数依赖线性矩阵不等式(Linear Matrix Inequality,LMI)中变参数Lyapunov矩阵与系统系数矩阵之间耦合乘积导致的非凸优化问题,转化为常规LMI约束下的凸优化问题进行求解,降低了线性变参数(Linear Parameter Varying,LPV)鲁棒滤波器设计的保守性,得到了全局解。针对涡扇发动机的仿真结果表明:与扩展卡尔曼滤波器对比,采用该方法设计的滤波器具有较快的动态跟踪速度和较高的滤波精度,ΔFn的稳态估计误差不大于0.1%,ΔFn的相对估计误差不大于2.5%,同时对建模误差和测量噪声干扰具有较强的抑制能力。     

Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.     

Optimum Signal and Filter Design in Underwater Acoustic Echo Ranging Systems

Optimization of the filter, the signal, and the signal and filter jointly are studied in the sonar environment under noise and reverberation limited conditions. The maximization of the receiver output signal-to-interference ratio is used as a performance criterion with unit energy constraint on both signal and filter. In the filter design problem, the optimum filter function is the solution of a linear integral equation. The kernel of the integral equation is a function of the target and medium scattering functions and the reverberation distribution. In the signal design problem, a similar type of integral equation is obtained as in the filter optimization problem. In the joint signal and filter design problem, it is shown that the optimum signal and filter functions are the solutions to a pair of linear integral equations with the largest (SIR)O. Several examples are investigated for different mediums and reverberation distributions with the finite matrix approximation method. An interative technique is used to compute the joint optimization of signal and filter.     

A three-state Kalman tracker using position and rate measurements

A three-state Kalman tracker is described for tracking a moving target, such as an aircraft, making use of the position and rate measurements obtained by a track-white-scan radar sensor which employs pulsed Doppler processing, such as the moving target detector providing unambiguous Doppler data. The steady-state filter parameters have been analytically obtained under the assumption of white noise maneuver capability. The numerical computations of these parameters are in excellent agreement with those obtained from the recursive Kalman filter matrix equations. The solution for the case when only the range measurements are available is obtained as a special case of this model. Graphs of normalized covariances and gains are presented to illustrate how the solution depends on different parameters     

Digital tracking filters with high order correlated measurement noise

The existing algorithms for the design of digital filters with colored measurement noise involve a restriction on the dimension of the measurement error model. Kalman filter equations and state space partition are used to formulate an optimal tracking filter without such restrictions. The input to the new filter are two consecutive measurements, and it is initialized by using the first available measurements and the error model correlation matrix. Several examples illustrate the filter formulation and initialization.     

Comments on “Discrete-time observability and estimabilityanalysis for bearings-only target motion analysis”

Discusses the extensive mathematical analysis carried out by the authors of the original paper [see ibid., vol. 33, no. 1, p. 178-201, 1997] and submits the following points. The authors used pseudo measurements for recasting the observability problem into a linear framework. They treated the bearings-only passive target tracking system as a deterministic system. It is already established that for deterministic systems, the pseudo measurements are linear functions of the states of the system, though the coefficient matrix is a nonlinear function of the original measurements, By using the pseudo measurements in a linear observer, global stability can be shown. However, if the pseudo measurement observer, for which the analysis is mostly carried out by the authors, is used in a noisy environment as a pseudo measurement filter (PMF), biased estimates are arrived at. Hence, though the approach of authors is quite direct and provides insights about the algebraic structure of the BOT problem, as pseudo measurements are used throughout the analysis is not of much use to the TMA community, as the nonlinear measurement equation along with measurement noise are required to be considered in the BOT problem to obtain unbiased results     

基于AEKF的永磁同步电机转速估计算法研究

针对在转速估算研究中采用常数矩阵不能准确描述永磁同步电机(PMSM)在不同运行条件下系统噪声的问题,提出了一种基于新息序列和状态残差的自适应扩展卡尔曼滤波算法(AEKF)。同时,对AEKF的稳定性进行理论上的探究。经仿真验证,与传统扩展卡尔曼滤波算法相比,AEKF在收敛速度和收敛精度上更优,参数鲁棒性更好。     

Simple Kalman gains for filtering position and velocitymeasurements

An alternate set of equations is given for the exact computation of the Kalman gains under the conditions of no maneuvering input noise and measurements in position and velocity. They are simpler than the standard recursive equations, and are useful in applications where implementation of the standard Kalman filter is not possible due to real-time restrictions. When there is maneuvering input noise, the same gains can still approximate the optimal gains with a very minor degradation in performance, even when some parameters, for example the measurement interval, change during a track. Simulation studies have indicated that there is negligible performance degradation with this method of gain approximation