A model-based prognostics method for fatigue crack growth in fuselage panels

This paper proposes a model-based prognostics method that couples the Extended Kalman Filter (EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled EKF-linearization method provides satisfactory results: the EKF algorithm well identifies the model parameters, and the linearization method gives comparable prediction results to Monte Carlo (MC) method while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful.     

Sensor-fault tolerant attitude determination using two-stage estimator

Satellite attitude determination accuracy significantly drops when sensor-fault occurs. Hence, a proper mitigation strategy to detect sensor-fault and accurately estimate corresponding fault magnitudes is mandatory for robust and accurate attitude determination. In this paper, a novel sensor-fault tolerant precise attitude estimator is proposed consisting of two stages. In the first stage, sensor-fault is detected, and the associated sensor parameter change is roughly estimated using an interacting multiple-model (IMM) approach. Subsequently, the second stage is triggered. The sensor parameter change is precisely estimated with a new sensor-parameter-augmented filter. This is defined as a selectively augmented extended Kalman filter (SAEKF) in this paper. The conventional augmented extended Kalman filter (AEKF) is computationally more expensive than the proposed SAEKF. The SAEKF augments only the sensor parameters affected by sensor-faults, not the full sensor parameters, into the state vector. This leads to a significant computational time-saving. A transition method from the first stage to the second stage is also investigated. Numerical simulation results demonstrate that the proposed two-stage approach has smaller attitude determination errors than the existing algorithms, ranged from 21.7% to 88.8%, in cases with gyro scale factor error or misalignment.     

运载火箭捷联惯组全自主对准技术应用研究

我国运载火箭发射前通常通过光学瞄准确定初始方位角,采用捷联惯组自对准解算获取水平姿态角。以可实现简易、快速发射的新型火箭为背景,在发射场阵风等干扰引起箭体低频微幅晃动的环境下,研究了捷联惯组自主对准技术。分析了运载火箭全自主对准的特点,利用以惯性系为参考基准的解析对准法和卡尔曼滤波精对准方法,对高精度全自主对准技术和其在运载火箭上的应用展开了详细论述。开展了全自主对准试验验证,结合新一代运载火箭首飞数据进行了分析。结果表明:捷联惯组全自主对准技术可替代复杂的光学瞄准系统,实现运载火箭发射前初始姿态的确定。     

Autonomous orbit determination using epoch-differenced gravity gradients and starlight refraction

Autonomous orbit determination via integration of epoch-differenced gravity gradients and starlight refraction is proposed in this paper for low-Earth-orbiting satellites operating in GPS-denied environments.Starlight refraction compensates for the significant along-track position error that occurs from only using gravity gradients and benefits from integration in terms of improved accuracy in radial and cross-track position estimates.The between-epoch differencing of gravity gradients is employed to eliminate slowly varying measurement biases and noise near the orbit revolution frequency.The refraction angle measurements are directly used and its Jacobian matrix derived from an implicit observation equation.An information fusion filter based on a sequential extended Kalman filter is developed for the orbit determination.Truth-model simulations are used to test the performance of the algorithm,and the effects of differencing intervals and orbital heights are analyzed.A semi-simulation study using actual gravity gradient data from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) combined with simulated starlight refraction measurements is further conducted,and a three-dimensional position accuracy of better than 100 m is achieved.     

一种新型的用于深空高动态微弱信号载波跟踪环

分析了深空通信中高动态微弱信号载波跟踪方法，提出并分析了载波跟踪中状态方程非线性与量测方程的非线性取舍问题，分析了滤波过程中影响高动态与低信噪比的决定因素，给出了两个推论，提出了强跟踪算法的必要性。并以此选择了以传统卡尔曼滤波（KF）为基础，以锁相环（FLL）、无迹卡尔曼滤波（UKF）为辅助使用两种工作模式来应对不同环境。针对组合跟踪环路状态转换过渡易出现的频率阶跃问题，给出新型环路的工作模式及切换门限的数学表达式，在环路中添加计算判别器，实时控制环路输出的同时不浪费计算力地剔除了野值，提高了环路稳定性。仿真表明，该新型环路算法与现有的算法相比有很好的低信噪高动态适应能力，消除了切换峰值，剔除了野值。     

Didactic satellite based on Android platform for space operation demonstration and development

Space technology plays a pivotal role in society development. It offers new methods for telemetry, monitoring and control. However, this sector requires training, research and skills development but the lack of instruments, materials and budgets affects the ambiguity to understand satellite technology. The objective of this paper is to describe a demonstration prototype of a smart phone device for space operations study. Therefore, the first task was carried out to give a demonstration for spatial imagery and attitude determination missions through a wireless communication. The smart phone’s Bluetooth was used to achieve this goal inclusive of a new method to enable real time transmission. In addition, an algorithm around a quaternion based Kalman filter was included in order to detect the reliability of the prototype's orientation. The second task was carried out to provide a demonstration for the attitude control mission using the smart phone’s orientation sensor, including a new method for an autonomous guided mode. As a result, the acquisition platform showed real time measurement with good accuracy for orientation detection and image transmission. In addition, the prototype kept the balance during the demonstration based on the attitude control method.     

无人直升机自主着舰系统设计及仿真试验

设计了一套包含视觉目标识别单元、目标跟踪器、甲板状态跟踪器和模糊逻辑控制的无人直升机自主着舰系统.视觉系统通过对采集的图像预处理提取出可能的甲板标识区域,然后对此区域计算其仿射不变矩特征集合,对得到的特征集经过模糊识别技术实现对甲板降落区域的识别,并根据矩特征集计算出甲板运动状态信息.目标跟踪器采用了卡尔曼滤波技术,在目标二阶运动方程的基础上设计了跟踪算法.甲板运动状态跟踪器用于在无人直升机的降落期间实现对甲板尤其是触舰瞬间甲板的纵摇、横摇及浮沉状态的预测,该跟踪器采用了改进的卡尔曼多步预测算法.设计了基于模糊逻辑的比例积分控制器,由多个并行的对特定任务实施控制的子控制行为构成.仿真试验结果验证了所提出的算法.在仿真试验中,无人直升机能够正确的识别甲板目标区域,实现了对运动舰船在二维水平面内的跟踪,甲板状态跟踪器能够预测未来1~5个周期内甲板状态,控制器基于目标信息和甲板状态能够正确地引导无人直升机实现安全着舰.     

陷波器对DLL的影响及其改进方法研究

陷波器被广泛应用于直接序列扩谱系统中滤除存在于系统工作频段内的干扰,分析 了陷波器对伪码跟踪环路的影响。针对陷波器引起的信号时延和畸变问题,提出对本地产 生的伪码也进行相应的陷波处理的方法来增加其与接收信号的相关性,同时针对信号畸变导 致的能量差值波动大的问题,提出增加能量积分时间的方法。在systemvue2006环境下对 提出的方法进行仿真验证,仿真结果表明这两种方法可以有效地削弱陷波器对伪码跟踪环 路的影响。
     

四元数UKF超紧密组合导航滤波方法

针对GPS/SINS(Global Positioning System/Strapdown Inertial Navigation Sys-tem)超紧密组合导航系统线性化滤波在高动态、弱GPS信号等环境下性能下降严重的问题,提出了一种以四元数UKF(Unscented Kalman Filter)为基础的GPS/SINS超紧密组合导航系统非线性滤波方法.通过分析GPS与SINS在超紧密组合导航系统中的关系,提出了一种利用SINS信息来估计、修正GPS载波信号的方法,在此基础上建立了组合导航滤波的状态方程和量测方程;以四元数乘性误差模型为基础,提出了一种可应用于UKF滤波的求解四元数加权均值的方法;提出了基于四元数UKF的GPS/SINS超紧密组合导航滤波方法.仿真实验表明:在高动态环境下,四元数UKF滤波方法增强了GPS/SINS超紧密组合导航系统的定位精度和抗干扰能力.     

自适应滤波在动态推力测量中的应用

动态推力测量过程中的干扰信号频谱经常与有用信号重叠,经典的模拟滤波或数字滤