估计理论在飞行数据相容性检验中的应用

飞行数据相容性检验在飞行试验数据分析中是很重要的。由于飞行试验测量数据中存在着各种过程噪声(如大气紊流)和测量噪声(如尺度因子偏差、常值系统偏差等),这些误差将会导致飞行数据各通道之间不相容。利用估计理论进行飞行状态估计,同时估计出未知的尺度因子,系统偏差,即进行飞行轨迹重构被认为是飞行数据相容性(一致性)检验的强有力的工具。本文利用推广卡尔曼滤波和信息平方根滤波及平滑进行高速飞机飞行数据的相容性检验,提出了飞机纵向运动和全面运动的新模型,通过仿真和实际计算得出满意的结果。     

一种实时飞行轨迹重构方法

提出一种有效的实时纵向飞行轨迹重构的新方法。为了得到状态估计的快速算法,本文把非线性飞行轨迹重构转化为线性、离散、时变状态和参数估计问题。将数值稳定性好、计算量也小的序列U-D分解滤波算法用于状态方程为线性、观测方程为线性或非线性的滤波问题中。由于测量值中常常含有系统偏差,本文把这些偏差作为增广状态加入增广状态模型中,并利用模型的一些特点,提出偏差分离的U-D分解算法,使计算量大大减少。仿真和实际试飞数据计算表明、本文的方法可得到比平方根协方差滤波更有效的实时飞行轨迹重构结果。     

基于边缘采样的简化高阶CKF在非线性快速传递对准中的应用

针对舰载机惯导系统非线性传递对准问题中误差模型不完善的问题，同时考虑了挠曲运动和动态杆臂的影响，提出了一种新的适用于大方位失准角情形下的挠曲变形和杆臂效应加速度一体化误差模型。采用高阶容积卡尔曼滤波（HCKF）算法对状态进行滤波估计，考虑到HCKF具有较大的计算量，分析了传递对准模型的状态方程与量测方程结构，设计了一种基于边缘采样的简化高阶容积卡尔曼滤波（M-RHCKF）算法，其在时间更新中使用边缘采样算法，在量测更新过程中使用简化量测更新过程，并给出了该算法的证明过程。采用"速度+姿态"组合匹配方式，对提出的误差模型进行仿真实验。结果表明，该模型可以满足对准精度和对准时间的要求，相比于未考虑动态杆臂的传递对准模型具有更高的对准精度。     

飞机起飞着陆性能智能计算模型及应用

提出了一种飞机起飞着陆性能智能计算思路。详细分析了影响飞机高原起飞着陆性能的主要因素,提出了高原起飞着陆性能智能计算的一般模型,然后采用支持向量机(Support Vector Machines,SVM)对某型飞机高原起飞滑跑距离实测数据进行了建模和验算,同时为说明支持向量机模型适合工程使用、精度高、推广性高的优点,还与贝叶斯正则化BP神经网络(BRBP)、RBF神经网络(RBF)、自适应神经模糊推理系统(ANFIS)做了比较。计算结果表明,支持向量机具有很好的推广性能,得到的结果优于BRBP,RBF和ANFIS等智能计算方法,推广误差能够满足工程的实际需要。该模型对于发展和丰富飞行器起飞着陆性能计算理论具有一定的参考价值。     

Enhanced motion and sizing of bank in moving-bank MMAE

The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard multiple model adaptive estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters, and dynamically redeclaring such discretization yields a moving-bank MMAE. A new online parameter discretization method is developed based on the probabilities associated with the generalized chi-squared random variables formed by residual information from the elemental Kalman filters within the MMAE. This new algorithm is validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.     

军用飞机生存力研究中的易损性分析

易损性的高低是评价生存力设计的一项重要指标。本文从易损性出发,明确建立一套计算、评估、分析飞机易损性的方法,并结合该方法,建立某型飞机的易损性分析模型,进行实例计算分析。通过计算分析,给出降低飞机易损性措施的建议,同时针对所存在的问题,提出个人的看法。     

Reconfigurable flight control via multiple model adaptive controlmethods

An aircraft flight control system with reconfigurable capabilities is considered. A multiple model adaptive controller (MMAC) is shown to provide effective reconfigurability when subjected to single and double failures of sensors and/or actuators. A command generator tracker/proportional-plus-integral/Kalman filter (CGT/PI/KF) form of controller was chosen for each of the elemental controllers within the MMAC algorithm and each was designed via LQG synthesis to provide desirable vehicle behavior for a particular failure status of sensors and actuators. The MMAC performance is enhanced by an alternate computation of the MMAC hypothesis probabilities, use of maximum a posteriori probability (MAP) versus Bayesian form of the MAC (or a modified combination of both), and reduction of identification ambiguities through scalar residual monitoring for the case of sensor failures     

Three-dimensional tracking using on-board measurements

The development of a three-dimensional tracker using onboard measurements is described. A system model based on aircraft dynamics is derived. A full 3-D tracking system that can be split, with a part operating onboard and a part operating on the ground, is developed. Attitude angle and aircraft airspeed measurements are processed to estimate the components of the aircraft velocity with respect to the surrounding air. These are then used to obtain estimates of the aircraft position and ground speed. The tracker is designed so that the number of quantities transmitted to the ground station is kept to a minimum. The tracker was evaluated with real data and was found to perform well, resulting in a considerable improvement over the conventional first-order Kalman filter     

多输入/多输出频域模态识别方法及在飞机地面振动试验应用

 本文介绍了一种多输入/多输出频域模态识别方法,并由计算机仿真和实际对称结构实例检验了本方法的精度、可靠性以及处理密集和重根模态的能力。文中给出了在某型现代歼击机全机地面振动试验中的应用,结果表明:和传统多点正弦激振相位共振技术相比,试验操作简单（无需调力）,试验周期短,识别精度高;和80年代发展的多输入/多输出时域方法相比,能直接识别系统的阶（模态数）,从而减少计算工作量,避免区分真实结构模态和“噪声模态”。     

大航向角误差情况下飞行中对准研究

为了解决飞行器在大航向角误差的情况下进行飞行中对准的难题,将一种线性大航向角误差模型应用于飞行器飞行中对准的过程.详细推导了线性大航向角误差模型,设计了飞行器在大航向角误差情况下进行飞行中对准的卡尔曼滤波模型,并进行了数字仿真.仿真结果表明,线性大航向角误差模型应用于飞行器飞行中对准,能够很好地解决飞行器在大航向角误差情况下进行对准的难点,并给出了适合于大航向角误差模型的机动方式.最后,提出了线性大航向角误差模型在工程应用中的几点建议     

基于改进滑动时间窗口的国产民用飞机APM参数筛选研究

合适的飞行性能监测（APM）参数筛选方法可实现国产民用巡航飞机性能监控参数的高效筛选,为飞机性能分析计算提供可靠的数据基础。在无迹卡尔曼滤波（UKF）中引入 Sage-Husa 噪声估计器,构造自适应无迹卡尔曼滤波（AUKF）,利用 AUKF 对快速存取记录器（QAR）数据进行降噪;给出稳定巡航参数筛选的标准,采用改进滑动时间窗口算法对稳定巡航参数进行筛选,并通过国产 ARJ21 飞机的样本数据进行验证。结果表明：自适应无迹卡尔曼滤波算法能够提高数据的可靠性,改进滑动时间窗口算法使筛选效率提高约 50%。     

基于四维导引的飞机纵向飞行剖面的解算与综合

定常Mach/CAS型下降是一种符合驾驶员实际操纵特性的剖面下降方法,可方便地用于对进入终端空域的飞机进行四维导引和控制。本文研究了这种四维飞行剖面的计算与综合技术,分析了剖面的构成与结构特点;讨论了剖面解算的数值积分算法和简化代数算法;对飞行速度剖面,提出了双参数描述法来扩大对飞行时间的调整范围,并开发了相应的迭代算法;最后以波音707飞机为对象,进行了数字仿真计算,得到了满意的结果。     

A Decision?Directed Adaptive Tracker

In the design of a tracking filter for air traffic control (ATC) applications, a maneuvering aircraft can be modelled by a linear system with random noise accelerations. A Kalman filter tracker, designed on the basis of a variance chosen according to the distribution of the potential maneuver accelerations, will maintain track during maneuvers and provide some improvement in position accuracy. However, during those portions of the flight path where the aircraft is not maneuvering, the tracking accuracy will not be as good as if no acceleration noise had been allowed in the tracking filter. In this paper, statistical decision theory is used to derive an optimal test for detecting the aircraft maneuver; a more practical suboptimal test is then deduced from the optimal test. As long as no maneuver is declared, a simpler filter, based on a constant-velocity model, is used to track the aircraft. When a maneuver is detected, the tracker is reinitialized using stored data, up-dated to the present time, and then normal tracking is resumed as new data arrives. In essence, the tracker performs on the basis of a piecewise linear model in which the breakpoints are defined on-line using the maneuver detector. Simulation results show that there is a significant improvement in tracking capability using the decision-directed adaptive tracker.     

Approximate Nonlinear Modeling of Aircraft Engine Surge Margin Based on Equilibrium Manifold Expansion

Stable operation of aircraft engine compressions is constrained by rotating surge. In this paper, an approximate nonlinear surge margin model of aircraft engine compression system by using equilibrium manifold is presented. Firstly, this paper gives an overview of the current state of modeling aerodynamic flow instabilities in engine compressors. Secondly, the expansion form of equilibrium manifold is introduced, and the choosing scheduling variable method is discussed. Then, this paper also gives the identification procedure of modeling the approximate nonlinear model. Finally, the modeling and simulations with high pressure (HP) compressor surge margin of the aircraft engine show that this real-time model has the same accuracy with the thermodynamic model, but has simpler structure and shorter computation time.     

高斯-厄米特滤波器在捷联惯导系统初始对准中的应用

 为提高静基座初始对准精度,缩短对准时间,采用了基于大方位失准角的对准模型,引入了高斯-厄米特滤波器(GHF)。针对GHF中均值和协方差阵的多元非线性高斯积分求解问题,利用初始对准误差方程的非线性是由大方位失准角导致的特点,通过状态的线性变换,求其线性状态解析解,将高维积分转化成一元数值积分,在不损失精度的前提下,解决了GHF在对准应用的"维数灾难"问题。将此算法用于实际系统,对比于扩展卡尔曼滤波器(EKF)、无迹卡尔曼滤波器(UKF),结果表明在大方位失准角条件下,GHF方法偏航角的对准精度提高了16%,对准时间缩短了75％。     

A Sampled Data Delay-Lock Loop Implemented as a Kalman Predictor

Performance data is presented for a sampled data delay-lock-loop incorporating a Kalman predictor. The jitter response due to noise and the deterministic response to aircraft maneuvers are investigated as a function of the Kalman model parameters. The predictor's transient response to step, ramp, and parabolic inputs and the number of iterations required for convergence for typical initialization errors are determined. It is shown how values for the model parameters may be chosen to obtain satisfactory noise, maneuver, and lock-up performance.     

μ Tools for flight control robustness assessment

The aim of this paper is to emphasize the usefulness of the μ framework for the analysis of the robustness properties of flight control systems. Model uncertainties may correspond, either to parametric uncertainties (e.g. in the aerodynamic model or in the value of the trim point) or to neglected high frequency dynamics (e.g. in the actuators or sensors). We especially show that several classical problems (computation of a phase or delay margin, non-linear analysis of a PIO effect, computation of a frequency domain overshoot) can be extended to an uncertain aircraft model. A flexible model of the aircraft can, moreover, be taken into account.     

基于Kalman滤波的变体飞行器T-S模糊控制

针对变体飞行器的跟踪控制问题,提出了一种基于Kalman滤波的T-S模糊控制方法。考虑飞行器系统状态不可测,引入惯导数据作为辅助信息,利用Kalman滤波算法融合飞控信息与惯导信息实现状态估计。由于变体飞行器在不同变形结构下气动特性变化较大,为便于控制器设计,采用小扰动线性化方法得到飞行器在不同平衡点处的局部线性模型,并通过状态反馈方法设计局部控制器,局部线性模型和局部控制器通过模糊集和模糊规则聚合成一个连续光滑的全局T-S模糊模型和T-S模糊控制器。通过综合Kalman滤波器与T-S模糊控制器得到一个基于Kalman滤波的T-S模糊控制器。仿真结果表明,该控制器在变形过程中能够实现状态估计,保证飞机的跟踪性能。     

飞机等效系统的在线辨识及其微机实现

引言 等效系统法是国外提出的评价带有复杂控制系统的飞机飞行品质的一种较有效的方法。它的基本思想是寻找一个带有等效时间延迟的低阶系统,使该系统具有或近似保留给定增稳飞机的高阶系统输出响应的主导特性,然后将低阶系统参数与规范要求相比     

Adaptive Two-Stage Extended Kalman Filter for a Fault-Tolerant INS-GPS Loosely Coupled System

The well-known conventional Kalman filter requires an accurate system model and exact stochastic information. But in a number of situations, the system model has an unknown bias, which may degrade the performance of the Kalman filter or may cause the filter to diverge. The effect of the unknown bias may be more pronounced on the extended Kalman filter (EKF), which is a nonlinear filter. The two-stage extended Kalman filter (TEKF) with respect to this problem has been receiving considerable attention for a long time. Recently, the optimal two-stage Kalman filter (TKF) for linear stochastic systems with a constant bias or a random bias has been proposed by several researchers. A TEKF can also be similarly derived as the optimal TKF. In the case of a random bias, the TEKF assumes that the information of a random bi?s is known. But the information of a random bias is unknown or partially known in general. To solve this problem, this paper proposes an adaptive two-stage extended Kalman filter (ATEKF) using an adaptive fading EKF. To verify the performance of the proposed ATEKF, the ATEKF is applied to the INS-GPS (inertial navigation system-Global Positioning System) loosely coupled system with an unknown fault bias. The proposed ATEKF tracked/estimated the unknown bias effectively although the information about the random bias was unknown.