Direct position determination using single moving rotating linear array: Noncoherent and coherent processing

To improve the resolution and accuracy of Direct Position Determination (DPD), this paper investigates the problem of positioning multiple emitters directly with a single moving Rotating Linear Array (RLA). Firstly, the geometry of the RLA is formulated and analysed. According to its geometry, the intercepted noncoherent signals in multiple interception intervals are modeled. Correspondingly, the MUltiple SIgnal Classification (MUSIC) based noncoherent DPD approach is proposed. Secondly, the synchronous coherent pulse signals are individually considered and formulated. And the coherent DPD approach which aims for localizing this special type of signal is presented by stacking all array responses at different interception intervals. Besides, we also derive the constrained Cramér-Rao Lower Bound (CRLB) expression for both noncoherent and coherent DPD with RLA under the constraint that the altitudes of the emitters are known. At last, computer simulations are included to examine the performance of the proposed approach. The results demonstrate that the localization accuracy and resolution of DPD with single moving linear array can be significantly improved by the array rotation. In addition, coherent DPD with RLA further improves the resolution and increases the maximum emitter number that can be localized compared with the noncoherent DPD with RLA.     

一种基于深度学习的电磁信息泄漏检测方法

电子信息设备工作时无意发射的电磁波中包含有用信息，会导致电磁信息泄漏，从而威胁设备的信息安全。现有的电磁信息泄漏检测方法，在复杂现场环境下，难以从具有不确定性的电磁泄漏信号中提取有用信息。面向电磁信息安全问题，开展了电磁信息泄漏检测研究，提出了一种基于深度学习的检测方法。设计了一个适用于电磁泄漏信号的一维卷积神经网络，并结合改进的梯度加权类激活映射方法，在未知电磁信息泄漏特征的前提下，通过深度学习实现电磁信息泄漏特征的智能标定和自动提取，从而解决了现场环境下电磁信息泄漏检测难以提取有用信息的问题。分别通过实测和仿真对比实验，验证了所提方法的有效性。      

液体火箭发动机健康监控技术研究现状

液体火箭发动机健康监控技术是改进和提高运载火箭、航天器可靠性与安全性的核心技术之一,对其进行研究具有重要的学术价值和工程应用价值。液体火箭发动机健康监控技术的研究主要包括液体火箭发动机故障检测与诊断理论方法、液体火箭发动机健康监控系统两方面。该文介绍了基于模型驱动的方法、基于数据驱动的方法和基于人工智能的方法,阐明了液体火箭发动机故障检测与诊断理论方法的研究现状,通过对美国液体火箭发动机典型健康监控系统的介绍,阐明了液体火箭发动机健康监控系统研究的若干进展及现状,并对液体火箭推进系统健康监控技术的演变趋势作了简要评述。     

Impact of rain on wave retrieval from Sentinel-1 synthetic aperture radar images in tropical cyclones

The main objective of our work was to investigate the impact of rain on wave observations from C-band (~5.3 GHz) synthetic aperture radar (SAR) in tropical cyclones. In this study, 10 Sentinel-1 SAR images were available from the Satellite Hurricane Observation Campaign, which were taken under cyclonic conditions during the 2016 hurricane season. The third-generation wave model, known as Simulating WAves Nearshore (SWAN) (version 41.31), was used to simulate the wave fields corresponding to these Sentinel-1 SAR images. In addition, rainfall data from the Tropical Rainfall Measuring Mission satellite passing over the spatial coverage of the Sentinel-1 SAR images were collected. The simulated results were validated against significant wave heights (SWHs) from the Jason-2 altimeter and European Centre for Medium-Range Weather Forecasts data, revealing a root mean square error (RMSE) of ~0.5 m with a 0.25 scatter index. Winds retrieved from the VH-polarized Sentinel-1 SAR images using the Sentinel-1 Extra Wide-swath Mode Wind Speed Retrieval Model after Noise Removal were taken as prior information for wave retrieval. It was discovered that rain did indeed affect the SAR wave retrieval, as evidenced by the 3.21-m RMSE of SWHs between the SAR images and the SWAN model, which was obtained for the ~1000 match-ups with raindrops. The raindrops dampened the wave retrieval when the rain rate was < ~5 mm/hr; however, they enhanced wave retrieval for higher rain rates. It was also found that the portion of the rain-induced ring wave with a wave number > 0.05 rad/m (~125 m wavelength) was clearly observed in the SAR-derived wave spectra.     

一种短航程再入解析预测校正制导方法

针对载人探月飞船高速再入返回问题，提出了一种短航程低过载的再入解析预测校正制导方法。引入大升阻比航天器滑翔式再入的概念，通过设定再入过程中滑翔段轨迹形式，利用轨迹参数描述滑翔段轨迹，推导出预测航程的解析公式。为使终端误差满足要求，通过试位法校正轨迹参数，并换算得到倾侧角制导指令。在偏差条件下进行仿真，实现了飞船2100km任务航程下400~450s内以低于6.5g₀的过载再入，结果表明，所提制导方法具有较高的精度和较强的鲁棒性，为载人探月飞船应急快速返回提供了参考思路。      

Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model

To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform (RFT), Dynamic Programming (DP), and Binary Integration (BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion. Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances.     

Reconfigurable fault-tolerant control for supersonic missiles with actuator failures under actuation redundancy

Aircraft undergoing actuator failures into under-actuation have been seldom studied in literature. Aiming at addressing actuator failures of Total Loss of Effectiveness (TLOE) as well as Partial Loss of Effectiveness (PLOE) resulting in different system actuations, reconfigurable Fault-Tolerant Control (FTC) is proposed for supersonic wingless missiles under actuation redundancy. The under-actuated system of TLOE failure patterns is solved by transformation to cascade systems through a ‘shape variable’. Meanwhile, actuator TLOE faults of different unknown failure patterns from proper actuation to under-actuation are accommodated by a reconfigurable adaptive law on a multiple-model basis. The backstepping technique with the Extended State Observer (ESO) method adopted as a basic strategy is applied to an established symmetric coupled missile system with actuator PLOE faults, modeling errors, and external disturbances. Additionally, the nonlinear saturation characteristics of actuators are settled by an auxiliary system with the Nussbaum function technique. The stability of the control system is analyzed and proven through Lyapunov theory. Numerical simulations are implemented in the presences of aerodynamic uncertainties, gust disturbance, and actuator failures. Results demonstrate the effectiveness of the proposed method with satisfactory tracking performance and actuator fault tolerance capacity.     

系统参数对电动力绳系动力学的影响

将电动力绳系(EDT)的主星质量、子星质量、绳系质量以及绳系中的电流视为系统参数，研究这些参数对系统的摆动动力学和轨道动力学的影响。哑铃模型下的电动力绳系摆动动力学方程存在不稳定的周期解，通过Floquet理论来衡量周期解的不稳定程度，从而研究各系统参数对摆动动力学的影响。建立了用春分点轨道元素的形式描述的电动力绳系轨道动力学方程，并以降轨时间来衡量电动力绳系的降轨效率，从而研究系统参数对轨道动力学的影响。运用算例对周期解迁移矩阵的特征值、降轨时间随各系统参数的变化关系进行了仿真，分别得出了各系统参数对系统摆动动力学和轨道动力学的影响。综合本文的仿真结果，并考虑实际发射及空间运行中的其它因素，对电动力绳系的设计和降轨策略提出了建议。     

一种基于张氏标定法的单目相机改进标定算法

单目视觉在地面机器人等无人导航系统中的应用越来越广泛，而相机参数的标定是利用视觉进行导航的基础性工作。标定结果的好坏，直接影响导航性能和定位精度。主要考虑单目相机的径向畸变，基于张正友平面标定法，通过利用Matlab进行图像预处理、利用OpenCV进行Harris角点检测和亚像素精确化等方法在角点提取精度等方面进行了改进，通过重投影法对标定结果进行了评估，并将其与Matlab相机标定工具箱Toolbox_calib所得结果进行了比较。实验结果表明，改进的标定算法标定结果精度更高，并且对多个摄像头传感器均有效，适用性强。     

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.