An optimal reduced-order stochastic observer-estimator

An optimal reduced-order observer-estimator (filter) is developed which can provide a full-dimensional vector of state estimates for systems where the dimension of the measurement vector is smaller than that of the state vector and none of the measurements are noise free. The reduced-order filter consists of two subfilters each of which provides a subset of the optimal estimate. A two-step L-K transformation is employed to minimize the estimate error variance of each subfilter. The optimal reduced-order filter developed is computationally efficient     

Square root parallel Kalman filtering using reduced-order localfilters

The basic parallel Kalman filtering algorithms derived by H.R. Hashemipour et al. (IEEE Trans. Autom. Control. vol.33, p.88-94, 1988) are summarized and generalized to the case of reduced-order local filters. Measurement-update and time-update equations are provided for four implementations: the conventional covariance filter, the conventional information filter, the square-foot covariance filter, and the square-foot information filter. A special feature of the suggested architecture is the ability to accommodate parallel local filters that have a smaller state dimension than the global filter. The estimates and covariance or information matrices (or their square roots) from these reduced-order filters are collated at a central filter at each step to generate the full-size, globally optimal estimates and their associated error covariance or information matrices (or their square roots). Aspects of computational complexity and the ensuing tradeoff with communication are discussed     

A reduced-order model for integrated GPS/INS

The dominant factor in determining the computation time of the Kalman filter is the dimension n of the model state vector. The number of computations per iteration is on the order of n³. Any reduction in the number of states will benefit directly in terms of increased computation time. In this paper, a high order model in integrated GPS/INS is described first, then a reduced-order model based on the high-order model, is developed. Finally, a faster tracking approach for Kalman filters is discussed. A typical aircraft trajectory is designed for a complex high-dynamic aircraft flight experiment. A Monte Carlo analysis shows that the reduced order model presented in this paper provides satisfactory accuracy for aircraft navigation     

Decentralized CFAR signal detection

The authors develop the theory of CA-CFAR (cell-averaging constant false-alarm rate) detection using multiple sensors and data fusion, where detection decisions are transmitted from each CA-CFAR detector to the data fusion center. The overall decision is obtained at the data fusion center based on some k out of n fusion rule. For a Swerling target model I embedded in white Gaussian noise of unknown level, the authors obtain the optimum threshold multipliers of the individual detectors. At the data fusion center, they derive an expression for the overall probability of detection while the overall probability of false alarm is maintained at the desired value for the given fusion rules. An example is presented showing numerical results     

自动编码器在流场降阶中的应用

自动编码器作为一种压缩算法,在数据降维和去噪等方面有着广泛实践,有条件作为一种降阶方法在流场识别与数据处理方面得到应用。文章中以圆柱绕流为例,首先对圆柱后速度场建立了编码模型,用来对原始数据进行降维和特征提取,之后将编码后的数据与流场特征量相关联,建立了由流场编码回归圆柱表面压力系数的神经网络,探索了降维后数据的应用。结果表明,自动编码得到的结果能够承载原始速度场的主要信息,解码后速度场与原速度场测试均方根误差小于0.02,压力回归测试均方根误差可小于0.1。说明自动编码器能够作为一种流场的特征提取和降阶方法,在未来得到更广泛的应用。     

基于CFD降阶模型的阵风减缓主动控制研究

飞行器飞行时会受到大气紊流的影响,降低飞行品质。阵风减缓控制是改善飞行器飞行性能的关键技术。现有的阵风响应分析多以离散阵风为研究对象,对更加真实描述大气紊流的连续型阵风时域分析关注较少。采用成形滤波器方法将频域形式给出的大气紊流信号转换为时域信号。在跨声速区域内,利用系统辨识技术,基于计算流体力学(CFD)方法建立阵风激励下的气动载荷状态空间降阶模型(ROM)。为方便控制器设计,借助平衡模态法进行模型的进一步降阶。使用模型预测控制(MPC)算法通过控制操纵面偏转实现阵风减缓主动控制。以AGARD445.6标模作为仿真算例,验证基于ROM设计的阵风减缓控制律的有效性。仿真结果表明,在跨声速飞行状态下,模型预测控制器能够在满足操纵面偏转范围的约束下,对连续阵风激励下的翼根弯矩输出进行有效抑制。     

适用于参数可调结构的非定常气动力降阶建模方法

基于计算流体力学(CFD)的非定常气动力降阶模型(ROM)可以极大提高气动弹性分析效率,然而现有的ROM只能针对固定参数结构,即只适合于固定模态振型,这使得现有ROM在气动弹性优化设计和不确定性分析等结构变参问题中应用受限。针对该问题,在文献[20]基础上提出了一种新的适用于任意模态振型的非定常气动力建模方法。首先将待设计/分析的结构进行参数化抽样和模态分析,之后通过主成分分析(PCA)得到若干基振型,再将这些基振型线性叠加即可拟合抽样空间内任何参数下结构的前若干阶振型。当结构参数改动时,仅仅是叠加系数发生变化。算例表明,仅用很少的基振型就能达到理想的拟合精度。经典的气动力降阶方法可用于基振型坐标下的气动力降阶,进一步变换可得到适用于不同结构的ROM,这意味着,结构参数可以在抽样空间内任意调节改动,而ROM却是通用的。该方法能广泛用于气动弹性优化设计和不确定性分析工作,可提高颤振分析精度和效率。     

Decentralized detection over multiple-access channels

We study the decentralized detection problem in a general framework where arbitrary number of quantization levels at the local sensors are allowed, and transmission from the sensors to the fusion center is subject to both noise and interchannel interference. We treat both Bayesian and Neyman-Pearson approaches to the problem, and develop an iterative descent algorithm to design the optimal quantizers and fusion rule. Some numerical examples for both approaches are also presented     

Non-intrusive reduced-order model for predicting transonic flow with varying geometries

A Non-Intrusive Reduced-Order Model (NIROM) based on Proper Orthogonal Decomposition (POD) has been proposed for predicting the flow fields of transonic airfoils with geometry parameters. To provide a better reduced-order subspace to approximate the real flow field, a domain decomposition method has been used to separate the hard-to-predict regions from the full field and POD has been adopted in the regions individually. An Artificial Neural Network (ANN) has replaced the Radial Basis Function (RBF) to interpolate the coefficients of the POD modes, aiming at improving the approximation accuracy of the NIROM for non-samples. When predicting the flow fields of transonic airfoils, the proposed NIROM has demonstrated a high performance.     

Matched Volterra reduced-order model for an airfoil undergoing periodic translation

This paper is devoted to application of the Reduced-Order Model(ROM) based on Volterra series to prediction of lift and drag forces due to airfoil periodic translation in transonic flow region. When there is large amplitude oscillation of the relative Mach number, as appeared in helicopter rotor movement in forward flight, the conventional Volterra ROM is found to be unsatisfactory. To cover such applications, a matched Volterra ROM, inspired from previous multistep nonlinear indicial response m...     

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。     

Identification of reduced-order model for an aeroelastic system from flutter test data

Recently, flutter active control using linear parameter varying (LPV) framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by syn-thesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant (LTI) models by using sys-tem identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency (p-LSCF) algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification, the dominant physical modes are determined by clear stabilization diagrams and clustering tech-nique. In the second step, with prior knowledge of physical poles, the improved frequency-domain maximum likelihood (ML) estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.     

无人机编队构成的分散最优控制方法研究

针对无长机带领、具有固定无向通信拓扑的无人机编队构成控制问题,提出了一种基于"相邻"无人机状态反馈的分散最优控制方法。该方法采用Laplacian矩阵描述编队通信结构,以"相邻"无人机与编队构型间的相对状态误差构建分散最优控制模型,并通过求解具有LMI约束的线性目标最优化问题得到编队各无人机的分散最优控制律。该方法使得多无人机在分散协同的前提下,基于局部信息快速准确地形成预定编队构型,达到运动方向和速度的一致性。仿真实验验证了该方法的有效性。     

领航跟随多导弹系统分布式协同制导

研究了具有领弹-从弹网络结构的多导弹分布式协同制导问题,首先运用代数图论和非线性系统一致性理论,分析了导弹局部通信拓扑与领弹-从弹网络化分布式协同制导系统性能之间的关系,进而设计了一类分布式的多弹协同制导律.该分布式协同制导可使领弹和从弹同时到达目标,且各相邻导弹间仅传输各自的可测状态信息,算法具有较低的通信代价和较好的可扩展性.最后给出了相关数值仿真算例,仿真结果验证了相关控制算法的有效性.     

自主多无人机的分散化协同控制

协同前提是无人机(UAV)平台间的通信和信息共享,无人机平台之间信息和计算是高度分布的,无人机平台的运动以及通信拓扑的变化,使得集中式协调控制结构很难实现。以最小通信量为基础的分散协同控制具有可扩展性、异构性和动态可重构性等特点,可靠性和鲁棒性较好。针对多无人机平台分散化协同的特点和要求,建立了集中和分散相结合的多无人机平台协同控制系统结构,集中式任务管理系统主要完成目标分配、通信管理和编队管理功能,分散式协同部分主要实现局部任务规划、协调策略及协调控制等功能。分别以多机协同目标跟踪、多机和多编队一致性协调、多机协同编队控制与重构等多无人机平台分散化协同控制技术为应用对象,探讨了分散化协调机制、策略、控制及其与信息之间关系。给出了部分算法的仿真结果。     

Decentralized linear estimation in correlated measurement noise

Some results and perspectives are provided on the problem of optimally combining estimates from different sensors when the measurement noise processes are correlated. The authors consider only the static estimation problem and limit the discussion to fusion with two sensors. A necessary and sufficient condition for optimality of the decentralized estimator in the presence of correlated measurement noise processes is presented. The result ties together previously reported work, and yields additional insights.<>     

基于时间反转和降阶Keystone的SAR-GMTI快速聚焦方法

对合成孔径雷达（SAR）地面运动目标聚焦技术进行了研究。针对现有运动目标聚焦方法存在的问题,提出了一种基于时间反转和降阶Keystone的SAR地面运动目标检测（SAR-GMTI）快速聚焦方法。首先,根据目标的机动特性建立了3阶距离模型;其次,针对目标多普勒中心模糊引起的多普勒谱分裂现象,结合所提时间反转处理（TRP）和降阶Keystone变换处理估计出运动目标2阶参数。此后,构造2阶相位补偿函数补偿运动目标的2阶距离徙动和多普勒徙动,从而完成运动目标的聚焦。同时,所提方法没有任何参数搜索过程,降低了计算复杂度。最后,仿真实验验证了所提算法的正确性和有效性。     

Decentralized Filtering and Redundancy Management for Multisensor Navigation

Failure detection and redundancy management is discussed for avionics applications of integrated navigation involving coordinated use of multiple simultaneous sensor subsystems such as GPS, JTIDS, TACAN, VOR/DME, ILS, an inertial navigation system (INS), and possibly even Doppler AHRS. A brief high level survey is provided to assess the status of those techniques and methodologies advertized as already available for handling the challenging real-time failure detection, redundancy management, and Kalman filtering aspects of these systems with differing availabilities, differing reliabilities, differing accuracies, and differing information content/sampling rates. Following the status review, a new failure detection/redundancy management approach is developed based on voter/monitoring at both the raw data and at the filtered-data level, as well as using additional inputs from hardware built-in-testing (BIT) and from specialized tests for subsequent failure isolation in the case of ambiguous indications. The technique developed involves use of Gaussian confidence regions to reasonably account for the inherent differences in accuracy between the various sensor subsystems. Online estimates of covariances from the Kalman filter are to be used for this purpose (when available). A technique is provided for quantitatively evaluating both the probability of detecting failed component subsystems and the probability of false alarm to be incurred, which is then to be traded off as the basis for rational selection of the thresholds used in the automated decision process. Moreover, the redundancy management procedure is demonstrated to be amenable to pilot or navigation operator prompting and override, if necessary.     

基于分散式RHC算法的无人机编队碰撞避免研究

提出了一种基于滚动时域控制(Receding Horizon control)的,保证无人机编队在飞行过程中满足约束条件并且防撞的分散式控制设计方案.此方法中,每架UAV都可以做出紧急自由碰撞规避机动,以保证冲突避免和约束履行.使用不变集合方法和强制协同的逻辑规则保证了在飞行中UAV之间的碰撞避免.仿真证明,此种分散式RHC方法是有效的.     

多无人机同时到达的分散化控制方法

多无人机(UAV)同时到达是典型的协同控制问题,在编队飞行、协同攻击中都有应用。以多无人机协同多目标攻击为应用背景,对多无人机同时到达问题进行了研究。考虑到战场环境的动态性和不确定性以及无人机自身的特点,提出一种适用于多无人机同时到达的分散化控制方法,其内容包括仅依靠局部信息交互的分散化控制结构和基于一致性算法的分散化控制策略。为方便操作员控制无人机群体的整体行为,分别设计了引入外部参考信号和虚拟Leader的分散化控制策略。根据路径规划和速度控制的不同特点将二者结合起来,利用它们的互补优势来应对路径误差和突发威胁等不利因素的影响。仿真结果表明,本文提出的分散化控制方法能够实现多无人机同时到达,并且具有很好的灵活性、鲁棒性、可靠性和可伸缩性。