Attitude control system design and on-orbit performance analysis of nano-satellite––‘‘Tian Tuo 1’’

‘‘Tian Tuo 1'(TT-1) nano-satellite is the first single-board nano-satellite that was successfully launched in China. The main objective of TT-1 is technology demonstration and scientific measurements. The satellite carries out the significant exploration of single-board architecture feasibility validation, and it is tailored to the low-cost philosophy by adopting numerous commercialoff-the-shelf(COTS) components. The satellite is featured with three-axis stabilization control capability. A pitch bias momentum wheel and three magnetic coils are adopted as control actuators.The sun sensors, magnetometers and a three-axis gyro are employed as the measurement sensors.The quaternion estimator(QUEST) and unscented Kalman filter(UKF) method are adopted for the nano-satellite attitude determination. On-orbit data received by ground station is conducted to analysis the performance of attitude determination and control system(ADCS). The results show that the design of ADCS for TT-1 is suitable, robust and feasible.     

基于改进LS-SVM的航空发动机传感器故障诊断与自适应重构控制

提出了一种基于改进LS-SVM的航空发动机传感器故障诊断与自适应重构控制方法.该方法通过给误差变量赋予不同权值因子提高LS-SVM的鲁棒性,采用修剪算法提高LS-SVM的稀疏性;该方法从某涡扇发动机输入输出空间中建立其正常模型,采用阈值判别法对传感器故障进行实时监视与诊断,并用模型输出值代替故障传感器测量值反馈回闭环控制系统,实现对发动机的自适应重构控制.仿真结果表明,该方法能及时准确地定位故障,并进行有效的自适应重构控制.      

三轴稳定卫星无陀螺姿态确定算法研究

针对三轴稳定卫星进行了正常飞行模式下无陀螺姿态确定算法研究，采用磁强计、地球敏感器和太阳敏感器组合获得姿态数据，利用四元数法和高斯－马尔可夫过程建立了姿态确定估计器算法模型。通过数学仿真证明，此姿态确定算法能够对星体姿态进行较精确的估计或校正，满足卫星控制精度要求。     

一种星敏感器-陀螺组合定姿的实时在轨标定方法

研究了一种星敏感器一陀螺组合定姿方式中的姿态敏感器误差的实时在轨标定方法。首先,选择直观的欧拉角作为姿态描述参数,根据星敏感器和陀螺的测量原理建立星敏感器一陀螺在轨标定的测量方程和状态方程,并以此建立数学模型。其次,采用简单高效的EKF（ExtendedKalmanFilter,扩展卡尔曼滤波）作为估值算法,进行了在轨标定数值仿真。对于航天器姿态定向中出现的姿态角和星敏感器安装角之间的耦合问题,通过在特定姿态通道上施加简单姿态机动实现了解耦。数值结果表明,该实时在轨标定方法,尤其是所提出的姿态角和星敏感器安装角解耦策略,可以实现对航天器姿态的实时精确估计以及对星敏感器安装误差、陀螺常值漂移和相关漂移等误差的实时在轨标定。该方法可用于航天器姿态测量设备的实时在轨标定和航天器姿态的高精度实时确定。     

Architectures and algorithms for track association and fusion

Target tracking using multiple sensors can provide better performance than using a single sensor. One approach to multiple target tracking with multiple sensors is to first perform single sensor tracking and then fuse the tracks from the different sensors. Two processing architectures for track fusion are presented: sensor to sensor track fusion, and sensor to system track fusion. Technical issues related to the statistical correlation between track estimation errors are discussed. Approaches for associating the tracks and combining the track state estimates of associated tracks that account for this correlation are described and compared by both theoretical analysis and Monte Carlo simulations     

基于遗传算法的机器人负载重力补偿优化算法研究

航空航天装配领域,利用安装在机器人末端的六维力传感器来感知外力是实现工业机器人柔顺装配的关键技术之一,而负载的存在会干扰对外力的感知。针对工业机器人末端负载的重力补偿,提出一种基于遗传算法的重力补偿优化算法。此方法以误差平方和最小为目标建立了最优解模型,利用遗传算法求解,最终可以在不使用测量仪器的情况下,估计力传感器安装偏角,提高重力补偿精度。同时设计了特定的机器人测量姿态来减少系统误差。试验表明,优化算法可以补偿任意角度的力传感器安装偏角,与补偿前相比,各方向最大重力补偿误差及平均重力补偿误差都有所减小。     

Mini-UAV altitude estimation using an inertially stabilized payload

A novel method is introduced for autonomous attitude estimation of a mini unmanned aerial vehicle (UAV) carrying an inertially stabilized payload. The method is based on utilizing the outputs of rate gyros normally used to inertially stabilize the payload, and other data that is normally available from conventional aircraft-mounted sensors. A decentralized estimation algorithm is developed, which uses the aircraft/payload mathematical models to bound the estimation errors. Exploiting modern multiprocessor computer technology, the new estimation algorithm comprises two parallel extended Kalman filters (EKFs) and a data fusion algorithm. Real-time experimental tests, incorporating a payload model with real rate gyros mounted on a three-axis flight table, have validated the feasibility of the concept. The theoretical and experimental investigation demonstrates that the estimation algorithm is capable of estimating the attitude angles with an estimation error not exceeding 1 deg, at output rates of 13 Hz, thus constituting a viable substitute for the conventional vertical gyroscope     

A fault tolerant model for multi-sensor measurement

Multi-sensor systems are very powerful in the complex environments.The cointegration theory and the vector error correction model,the statistic methods which widely applied in economic analysis,are utilized to create a fitting model for homogeneous sensors measurements.An algorithm is applied to implement the model for error correction,in which the signal of any sensor can be estimated from those of others.The model divides a signal series into two parts,the training part and the estimated part.By comparing the estimated part with the actual one,the proposed method can identify a sensor with possible faults and repair its signal.With a small amount of training data,the right parameters for the model in real time could be found by the algorithm.When applied in data analysis for aero engine testing,the model works well.Therefore,it is not only an effective method to detect any sensor failure or abnormality,but also a useful approach to correct possible errors.     

基于深度森林的卫星ACS执行机构与传感器故障识别

针对卫星姿态控制系统（ACS）闭环回路的故障难以辨识的问题,引入深度森林算法,实现执行机构与传感器故障识别。首先针对可获取的少量卫星姿态控制系统遥测数据,结合系统动力学特性,研究合适的特征选择和特征提取方法,再结合深度森林算法进行故障信息学习与辨识,建立故障预测模型,实现执行机构故障与传感器故障的识别。半物理仿真结果表明：在存在气浮台干扰力矩、卫星转动惯量未知、飞轮非线性特性、闭环故障传播等多种不利因素情况下,深度森林算法对于执行机构和传感器故障具有高效的识别能力。     

磁强计数据辨识卫星自旋状态

针对近地低轨三轴稳定卫星在轨管理后期,除磁强计外其他姿态敏感器都无效情况下的姿态异常问题,分析了三轴稳定卫星失去姿态基准后,星体自旋状态下三轴磁强计测量数据的特点,提出了使用磁强计测量数据的矢量信息,以找到能够获取卫星状态的方法,从而建立了磁强计测量矢量与卫星自旋轴的几何关系,给出了处于自旋状态下的卫星自旋轴确定方法。通过此种辨识方法,获得了某气象卫星姿态异常翻转状态下的自旋矢量方向和自旋角速度,从而证明了该辨识方法快速、有效,可以作为姿态异常卫星自旋状态的辨识手段,为恢复卫星姿态提供了重要信息,具有一定的工程应用价值。     

航空发动机双重传感器故障诊断逻辑研究

针对航空发动机控制系统的双重传感器故障,提出了一种采用双路容错设计的卡尔曼滤波器故障检测隔离系统.故障检测隔离系统由一系列卡尔曼滤波器组成,每个滤波器都假定2路传感器故障,而以故障支路外的测量值作为输入量.当双重传感器故障发生时,只有不包含故障传感器信息的滤波器保持较低的估计残差,其他滤波器都会产生较大的估计残差,如此双重传感器故障便可以被隔离.利用滤波器组估计残差的特征,进一步设计合理的运算逻辑,系统就可以同时对传感器单一故障进行检测和隔离.为了验证故障诊断系统的有效性,在发动机慢车状态分别对传感器发生双重故障和单一故障的情况进行仿真.仿真结果表明:故障诊断系统能够准确有效地对传感器双重故障和单一故障进行检测和隔离.      

航空发动机传感器解析余度模型的建立方法

为了使用解析余度模型对传感器故障进行诊断,提出了1种基于K-均值聚类与改进微分进化算法优化的极端学习机(IDE-ELM)的发动机传感器解析余度模型建立方法。为避免求解ELM算法时H矩阵奇异,采用K-均值聚类对试验数据进行聚类处理,然后从每类数据中选取1组数据组成训练样本用于训练;利用IDE算法优化ELM的输入层权值和偏置,提高ELM的泛化能力。利用飞行试验数据进行了仿真验证。结果表明:基于K-均值聚类和IDE-ELM设计的传感器解析余度模型具有较高的精度,可用于FADEC系统双通道传感器的故障诊断。     

Tracking a maneuvering target using sensors of variable quality

In an environment subject to sudden change, the accuracy of tracking and prediction is strongly influenced both by the sensor architecture and by the quality of the sensors. An image-enhanced algorithm is presented for both path following and covariance estimation in applications where the sensors are subject to sudden and unpredictable variation in quality. For an illustrative trajectory, the performance of the algorithm is contrasted with an extended Kalman filter (EKF) and an image-enhanced algorithm based upon the nominal sensors     

STUDY ON ALGORITHM OF SENSOR MANAGEMENT BASED ON FUNCTIONS OF EFFICIENCY AND WASTE

The sensor management system is a subsys-tem of a multisensor data fusion system,and itspurpose is to satisfy requests of multitarget andscanned space by using the limited sensor resourcesin order to gain optimal measurement values of allspecified characteristics ( detection and captureprobability,emission power of sensor,trackingprecision or target losing probability and so on) .By the optimal principle listed above,sensor re-sources are distributed in science and reason.In aword,itis a key p…     

Fusion of sensors with dissimilar measurement/tracking accuracies

The case of data fusion of sensors dissimilar in their measurement/tracking errors is considered. It is shown that the fused track performance is similar whether the sensor data are fused at the track level or at the measurement level. The case of a cluster of targets, resolved by one sensor but not the other, is also considered. Under certain conditions the fused track may perform worse than the worst of the sensors. A remedy to this problem through modifications of the association algorithm is presented     

A Vector Autopilot System

Current technology has evolved low cost, highly reliable solid state vector magnetometers with excellent angular resolution. This paper discusses the role of a three-axis magnetometer as a new instrument for aircraft attitude determination. Using flight data acquired by an instrumented aircraft, attitude is calculated using the Earth's magnetic field vector and compared to measured attitudes. The magnetic field alone is not adequate to resolve all attitude variations and the need for a second reference angle or vector is discussed. A system combining the functions of heading determination and attitude measurement is presented to show that both functions can be implemented with essentially the same component count required to measure heading alone. lt is concluded that with the correlation achieved in calculated and measured attitude there is a potential application of vector magnetometry in attitude measurement systems.     

光学三角法测头的精度优化技术研究

为了提高光学三角法测头在实际应用中的测量精度,本文在分析光学三角法测量原理的基础上,提出了一种基于自适应区域平均中点匹配算法的光学测头精度优化方法。该方法以空间域滤波思想为基础,可以根据测头的测量值自动判断测量误差的大小以及优化区域的大小,并可以针对不同表面给出相应的精度优化方法。实验结果表明,该优化算法可以准确描述被测物体表面的轮廓特征,并且能够降低测量误差50%以上,从而有效解决了光学扫描测量精度不足的问题,使测头能够达到微米级的测量精度,实现了零件的高效率、高精度测量。     

Direction-of-arrival estimation using signal subspace modeling

A computationally viable algorithm for estimating the direction-of-arrival (DOA) of multiple wavefields that are incident on an array of sensors is developed. The geometry of the array is unrestricted and the incident wavefields may be generated by mixtures of incoherent and coherent emitting sources. In the approach taken, the sensor signals are modeled as a noise-contaminated linear combination of steering vectors which are functionally dependent on a set of DOA parameters. These parameters are to be chosen so that this sensor signal model is most compatible with empirically measured data (i.e., snapshot data). An iterative procedure is developed for selecting the most data compatible set of DOA parameters in both the snapshot domain and the array covariance matrix domain. Critical to the success of such iterative solution procedures is the generation of quality DOA parameters to initialize the algorithm. A sequential beamforming method for this initialization is presented. Numerical examples to illustrate the effectiveness of the proposed algorithmic approach are given     

KUPS: Knowledge-Based Ubiquitous and Persistent Sensor Networks for Threat Assessment

We propose a knowledge-based ubiquitous and persistent sensor network (KUPS) for threat assessment, in which "sensor" is a broad characterization. It refers to diverse data or information from ubiquitous and persistent sensor sources such as organic sensors and human intelligence sensors. Our KUPS for threat assessment consists of two major steps: situation awareness using fuzzy logic systems (FLSs) and threat parameter estimation using radar sensor networks (RSNs). Our FLSs combine the linguistic knowledge from different intelligent sensors, and our proposed maximum-likelihood (ML) estimation algorithm performs target radar cross section (RCS) parameter estimation. We also show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer-Rao lower bound (CRLB) if the radar pulses follow the Swerling II model. Simulations further validate our theoretical results.