固有模态函数（IMF）积检测器——以低信噪比情况下超宽带雷达信号检测为例

首次提出了一种固有模态函数积检测器。首先通过经验模式分解（EMD）把带噪信号分解成有限个固有模态函数（IMF）。检测的基本思路是，对各个IMF分量的绝对值作逐点乘积，用于抑制噪声并凸现信号，最后进行滤波和判决。本文以UWB信号为例，数据源于UWB雷达实验系统。在低信噪比（SNR），UWB脉冲与噪声波形相似，且噪声概率密度函数（PDF）未知情况下，进行实验。结果表明，当峰峰信噪比低于5dB时，该检测器性能优于Teager能量算子（TEO）。     

On the convergence of iterative methods for bearings-only tracking

This paper deals with the analysis of the convergence of iterative methods for bearings only tracking (BOT). A geometric and unified framework is developed. Explicit sufficient conditions ensuring convergence of iterative methods for maximization of the likelihood functional are then derived     

Bearings-only tracking for maneuvering sources

Classical bearings-only target-motion analysis (TMA) is restricted to sources with constant motion parameters (usually position and velocity). However, most interesting sources have maneuvering abilities, thus degrading the performance of classical TMA. In the passive sonar context a long-time source-observer encounter is realistic, so the source maneuver possibilities may be important in regard to the source and array baseline. This advocates for the consideration and modeling of the whole source trajectory including source maneuver uncertainty. With that aim, a convenient framework is the hidden Markov model (HMM). A basic idea consists of a two-levels discretization of the state-space. The probabilities of position transition are deduced from the probabilities of velocity transitions which, themselves, are directly related to the source maneuvering capability. The source state sequence estimation is achieved by means of classical dynamic programming (DP). This approach does not require any prior information relative to the source maneuvers. However, the probabilistic nature of the source trajectory confers a major role to the optimization of the observer maneuvers. This problem is then solved by using the general framework of the Markov decision process (MDP)     

The Solar Sail Materials (SSM) project – Status of activities

SSM (Solar Sail Materials) is an on-going project for the European Space Agency (ESA) relying on past and recent European solar sail design projects. It aims at developing and testing future technologies suitable for large, operational solar sailcrafts.     

Target motion analysis with multiple arrays: performance analysis

The performance analysis of source trajectory estimation using measurements provided by multiple platforms (or arrays) is studied. In numerous practical situations, the maneuvering ability of the receiver (e.g., a ship towing linear arrays) is limited, leading to the assumption that the observer motion is rectilinear and uniform. Even if this hypothesis appears quite restrictive, practical and tactical considerations fully justify its interest. This leads to consider multiple (platform) target motion analysis (denoted MTMA) and to analyze the performance of such trajectory estimation methods     

PR状态方程在超临界喷射模型中的应用

针对超临界流体物性的特殊性,对超临界喷射数值模拟方法进行研究。基于PR状态方程建立了考虑超临界流体特点的超临界喷射数值模型,并采用该模型对超临界C10H22喷射到超临界N2环境中的喷射进行了数值模拟。对比研究了采用PR状态方程的真实气体模型和理想气体模型得出的密度、温度、质量分数分布以及超临界喷射长度和喷射扩张角的变化规律和差异性,并与试验数据进行了对比。结果表明:2种模型在物性预测上的差异会造成以上喷射特性模拟结果的巨大差异,理想气体模型模拟结果与试验数据误差很大,利用真实气体模型能够得到与试验数据较为吻合的结果。基于PR状态方程的超临界喷射数值模型准确可靠,可为碳氢燃料的超临界喷射现象提供参考。     

波音777飞机ACARS系统及马航MH370航班失联事件分析

马来西亚航空公司MH370客机失联事件一度引发媒体与公众的诸多猜测,猜测的焦点之一就是执飞的波音777飞机的飞机通信寻址与报告系统(Aircraft Communication Addressing and Reporting System,简称ACARS)功能是如何被关闭的。从专业角度出发,首先采用Sys ML建模的方式描述了波音777飞机ACARS系统的详细架构和人机接口,并据此深入分析了马航MH370航班失联事件中ACARS被关闭的情景。最后,提出了此次事件暴露出的现代民用飞机设计中存在的问题以及改进方法。     

纳米碳粉掺杂对激光烧蚀GAP的推进性能影响

实验以含能聚合物聚叠氮缩水甘油醚(Glycidyl azide polymer,GAP）作为激光烧蚀微推力器的靶材。通过对不同浓度纳米碳粉掺杂和靶材厚度下激光烧蚀GAP的比冲、冲量耦合系数和能量转化效率测量，结合靶材喷射羽流图像，分析了纳米碳粉掺杂提高激光烧蚀聚合物靶材推进性能的机理，给出纳米碳粉掺杂的适用方式。实验结果表明：透射式下，掺杂纳米碳粉之后，聚合物对激光的吸收大幅增强，但激光烧蚀推进性能不随掺杂浓度增加而显著提升；纳米碳粉吸收激光能量形成温度极高的局部热区促进聚合物中化学能的释放，是推进性能提升的主要原因；掺杂纳米碳粉之后的GAP烧蚀深度降低，表现出面吸收特性；随着靶材厚度的增加，未完全烧蚀的工质质量增加，使得靶材的利用率大大降低，导致聚合物推进性能下降。实验中掺杂3%纳米碳粉、厚度为54 μm的GAP靶材最优能量转化效率超过250%，适合作为透射式激光烧蚀微推力器的靶材。     

Modeling irregular small bodies gravity field via extreme learning machines and Bayesian optimization

Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface.     

C/C复合材料喷管烧蚀壁面退移流固耦合仿真研究

为了保障固体火箭发动机C/C喷管的可靠性,建立了一套正确反映发动机喷管烧蚀过程的流固耦合计算模型,以实现对喷管烧蚀率的高精度预估。依据热化学烧蚀理论以及喷管内燃气与喷管结构体界面的质量平衡和能量平衡关系,建立并验证了考虑壁面退移的C/C喷管流固耦合方法,实现了燃气流动、异相化学反应、结构体传热三者间的耦合。通过实验发动机喷管的烧蚀计算,论证了模型的正确性,并分析了不同金属铝含量对烧蚀率的影响,计算所得的烧蚀率与实验值最大相对误差为4.3%,与不考虑壁面退移的耦合算法计算结果对比,计算精度最高可提升46%。计算结果表明：C/C喷管在喉部附近烧蚀最为严重;推进剂中Al含量的增加导致燃气中氧化组分浓度降低,进而减少了烧蚀速率,这些结论与C/C喷管烧蚀相关研究结果一致。