一种适用于跑步状态的惯性/零速/GPS室内外无缝组合导航定位方法

目前，行人导航定位技术已经深入社会的众多领域，受到诸多学者的广泛关注。针对行人跑步状态，研究了一种惯性/零速/GPS室内外无缝组合导航定位方法。首先提出了可靠的、适用于行人跑步零速检测的方法，有效提高了在行人跑步状态下的零速检测的准确性。针对GPS信号容易受到高楼、高架等环境的干扰及在室内容易完全丢失的特点，提出了基于BP神经网络的GPS可用信号筛选方法，提高了GPS信息的可靠性与精准性。在此基础上，研究了基于可变量测的Kalman滤波器，实现了惯性/零速/GPS信息的有效融合，显著提高了在行人跑步状态下的导航定位精度。试验结果表明，所提出的这种适用于跑步状态的惯性/零速/GPS室内外无缝组合导航定位方法的平均定位误差可减小到行人跑步总里程的1%以内。     

Probabilistic resident space object detection using archival THEMIS fluxgate magnetometer data

Recent progress in the detection of small space objects, at geosynchronous altitudes, through ground-based optical and radar measurements is demonstrated as a viable method. However, in general, these methods are limited to detection of objects greater than 10?cm. This paper examines the use of magnetometers to detect plausible flyby encounters with charged space objects using a matched filter signal existence binary hypothesis test approach. Relevant data-set processing and reduction of archival fluxgate magnetometer data from the NASA THEMIS mission is discussed in detail. Using the proposed methodology and a false alarm rate of 10%, 285 plausible detections with probability of detection greater than 80% are claimed and several are reviewed in detail.     

Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data

Attitude estimation is a critical component of the Attitude Determination and Control System (ADCS) of any satellite. It is used to convert the sensor observation data to an estimated attitude using filtering algorithms. However, in the presence of sensor faults, the ADCS fails to achieve the desired attitude accuracy. In this paper, the Fault Tolerant Extended Kalman Filter (FTEKF) is proposed to handle this imperfection. In accordance, various filtering steps are included in the FTEKF design to enhance both attitude estimation and sensor fault detection. The developed algorithm can detect and isolate any unexpected sensor faults in real time, which provides a reliable attitude estimation. A comparative study with the classical and robust Kalman filters is performed through numerical simulations in order to validate the effectiveness of the adopted filter in case of magnetometer fault data.     

UKF参数估计在航天器气动辅助变轨问题中的应用

为快速精确地求解气动辅助变轨问题，提出一种基于无损卡尔曼滤波(UKF)参数估计的数值求解方法。首先，针对气动辅助变轨问题，利用极大值原理将其转化为对应的两点边值问题;然后，以协态变量的初值作为估计参数，以末端条件为期望观测值，将该两点边值问题转化为参数估计问题，并应用UKF滤波算法求解。该算法基于估计理论，避免了计算传统数值方法所需要的梯度矩阵，同时克服了猜测协态变量初值的困难，降低了求解气动辅助变轨问题的难度。数值仿真表明，该算法结构简单，求解效率高，具有良好的鲁棒性。     

The evaluation of damped Newton variational ocean wind retrieval method

This paper researches on Synthetic Aperture Radar (SAR) ocean wind retrieval approach, evaluates the method of Damped Newton VARiational method (DNVAR) and the direct method (DIRECT) using two years’ real SAR data and different background winds. Experiments are carried out using SENTINEL-1 SAR information as observation, National Data Buoy Center (NDBC) buoy data as in-situ data, Global Forecast System (GFS), Advanced SCATterometer (ASCAT), WindSAT, and Cross-Calibrated Multi-Platform (CCMP) wind products as background data. Thousands of match-ups are extracted. Results show that DNVAR can improve the wind field accuracy when the background data is ASCAT and CCMP. Especially, DNVAR wind speed results are much better than the official product of SENTINEL-1 wind data. The background data is crucial to the wind field retrieval results, so high accuracy of background data could lead to high accuracy results.     

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.     

A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.     

滤波器群时延对二元翼段阵风减缓的影响

针对二元翼段阵风载荷减缓主动控制系统,研究了数字滤波器群时延对阵风减缓效率的影响。首先建立了气动弹性模型,并将时滞系统转化为等价的无时滞系统。随后设计了阵风发生器、数字滤波器及时滞线性二次型(Linear quadratic,LQ)控制器等组成控制系统。最后对系统进行数值仿真并开展风洞实验加以验证。研究发现,当滤波器群时延量较小时可以改善控制系统性能,提高阵风载荷减缓效率,但随着滤波器群时延量增大,阵风载荷减缓效率降低甚至出现控制器失效的情况。     

涡扇发动机部件特性的滤波自动修正更新方法

针对航空发动机部件制造装配以及性能蜕化引起的平均模型与个体发动机之间的性能不匹配问题,提出一种基于非线性滤波算法的发动机部件特性自动修正方法。根据发动机部件级平均模型输出与个体量测数据的残差,利用数据处理策略结合无迹卡尔曼滤波算法的不可测部件特性变化估计,自动更新发动机部件特性,建立发动机个体物理模型。通过小涵道比涡扇发动机仿真验证,结果表明该方法可自动修正发动机部件特性,相比较平均模型,通过该方法修正的发动机个体模型中各截面温度、压力计算偏差均在0.5%以内,有效提高涡扇发动机个体物理模型稳态、动态精度。     

基于旋转滤波矩阵束的异步电机转子断条故障诊断

现有异步电机故障诊断技术存在短时数据分辨率低、硬件开销大等缺点。针对这一问题,提出一种基于短时数据的旋转滤波矩阵束的异步电机转子断条故障诊断新方法。利用矩阵束算法抗噪性能强的优点,准确求出定子电流基频成分,并通过逆、正同步旋转变换,剔除了定子电流中的基频成分。利用矩阵束算法准确估算定子电流故障信号的频率和幅值,突破传统基于快速傅里叶变换(FFT)分析算法分辨率不足的限制。仿真和电机试验共同表明:旋转滤波矩阵束算法可以在短时数据的基础上准确辨识转子断条故障。