航天器火工冲击环境数据外推技术研究

为了制定可靠的组件冲击试验条件,需要预示航天器火工冲击环境.在分析了航天器火工冲击环境预示的三类方法,包括试验法、数值分析法和数据外推法的基础上,详细总结和分析了数据外推法的国内外研究进展,并通过理论推导提出了新的数据外推公式;以大量火工冲击测量数据为参照,通过对比分析证明该数据外推公式比NASA和ESA的数据外推公式...     

基于偏差自适应学习的多传感器信息融合机动目标跟踪

为了保证精确打击机动目标,导弹可以采用主/被动雷达切换探测目标。考虑到作战的隐蔽性和生存性,提出基于多传感器信息融合的被动优先跟踪方法:跟踪开始时,令主/被动雷达同时对目标进行探测和跟踪,将二者的信息进行融合,同时自适应地学习融合结果与二者信息的偏差,经过一段时间学习,融合偏差稳定,此时令主动雷达停止工作,由被动雷达单独工作,而目标的运动信息则由被动雷达的信息和学习得到的融合偏差合成。如果目标机动较大,则定期令主动雷达工作以进一步修正融合偏差。该方法既保证了跟踪的精度,同时又减少了主动雷达的工作时间,从而提高了作战的隐蔽性和生存性。将该方法应用于导弹的目标跟踪,仿真结果表明该方法有效。     

大气探测激光雷达组网观测一致性研究

通过激光遥感获取气溶胶、云垂直分布情况，对研究气溶胶、云微物理特性、辐射强迫效应以及污染物传输等具有重大意义。在实际应用中激光雷达受激光器能量、发散角以及透过率等影响，会导致各雷达面对同一目标物探测数据不一致。随着地基大气探测激光雷达逐步规模化、标准化，对雷达组网观测一致性开展研究具有重要意义。为保证组网激光雷达数据高质量和高可靠性，在雷达系统自标定基础上，利用太阳光度计、大气分子模型获取激光雷达系统常数，确保各激光雷达回波信号定量可比。通过激光雷达组网观测进行探测目标一致性比对试验，以验证激光雷达系统探测一致性精度。结果表明：标定后，532 nm距离修正信号在1~2 km区间相对偏差从64.89%降低到22.16%，在2~5 km区间相对偏差从49.26%降低到8.90%，在9.5~11.5 km相对偏差从46.83%降低到10.91%；532 nm退偏比在1~2 km区间相对偏差从69.68%降低到20.68%，在2~5 km区间相对偏差从71.24%降低到6.69%，在9.5~11.5 km相对偏差从140.24%降低到9.02%；532 nm后向散射系数在1~2 km区间相对偏差从37.45%降低到23.63%，在2~5 km区间相对偏差从29.15%降低到21.45%，在9.5~11.5 km相对偏差从76.02%降低到24.16%。组网激光雷达数据结果一致性较好，可在多站点进行高精度探测，在气候变化研究、碳排放监测和环境研究等领域发挥重要作用，为大规模大气变化规律研究提供高质量、有效数据。     

An online data driven actor-critic-disturbance guidance law for missile-target interception with input constraints

In this article, we develop an online robust actor-critic-disturbance guidance law for a missile-target interception system with limited normal acceleration capability. Firstly, the missiletarget engagement is formulated as a zero-sum pursuit-evasion game problem. The key is to seek the saddle point solution of the Hamilton Jacobi Isaacs(HJI) equation, which is generally intractable due to the nonlinearity of the problem. Then, based on the universal approximation capability of Neural Networks(N...     

智慧火箭数据获取技术探索与展望

随着智慧火箭概念的提出,对运载火箭数据获取技术提出新的挑战,目前,运载火箭存在测量领域受限、测量精度不高、可靠性不高、可扩展性差和可裁剪性不高等问题。结合数据获取现状和问题分析,总结出未来数据获取朝着智能化、网络化和轻质高效化方向发展的趋势。结合趋势研判给出某火箭的数据获取总体框架,并对结构健康监测、光纤传感网络、无缆化等关键技术的优缺点和应用场景进行对比分析,提出了光纤光栅传感网络、无线传感网络、磁谐振式无线传能等技术在智慧火箭数据获取上的应用展望。     

Few-shot electromagnetic signal classification: A data union augmentation method

Deep learning has been fully verified and accepted in the field of electromagnetic signal classification. However, in many specific scenarios, such as radio resource management for aircraft communications, labeled data are difficult to obtain, which makes the best deep learning methods at present seem almost powerless, because these methods need a large amount of labeled data for training. When the training dataset is small, it is highly possible to fall into overfitting, which causes performance degradation of the deep neural network. For few-shot electromagnetic signal classification, data augmentation is one of the most intuitive countermeasures. In this work, a generative adversarial network based on the data augmentation method is proposed to achieve better classification performance for electromagnetic signals. Based on the similarity principle, a screening mechanism is established to obtain high-quality generated signals. Then, a data union augmentation algorithm is designed by introducing spatiotemporally flipped shapes of the signal. To verify the effectiveness of the proposed data augmentation algorithm, experiments are conducted on the RADIOML 2016.04C dataset and real-world ACARS dataset. The experimental results show that the proposed method significantly improves the performance of few-shot electromagnetic signal classification.     

无人机集群组网高精度测距技术研究

针对无人机组网系统机间测距精度受限于时域采样周期时延估计的问题，本文提出一种联合正交频分复用（OFDM）前导序列和导频序列进行高精度测距估计的方法，利用最大似然估计和双向测距法将参与计算的序列测距估计结果按比例进行加权平均。同时，仿真分析了联合测距估计方法性能。结果表明：联合估计方法的前导与导频序列差分自相关间隔长度取典型值时测距精度能达到0.1 m以下，可在较低信噪比下实现厘米级测距精度，提升了集群组网测距的稳定性。     

一类基于可监管区块链的公共安全信息共享平台方案设计

公共安全涉及到应急管理、供应链管理和医疗健康等领域,其数据共享平台对提供全方位、全过程的公共安全服务具有重要意义,尤其在当前的疫情背景下.但目前,由于涉及敏感数据的隐私保护,公共安全数据共享平台的架构设计一直面临着严峻的挑战.基于通过智能合约实现工作流协调和访问控制机制的设计思路,给出了一类基于可监管区块链的新型公共安...     

面向自动驾驶仿真的虚拟LiDAR传感器建模

在自动驾驶仿真领域，虚拟传感器输出数据的精准度是仿真结果可靠性的重要保障。激光雷达（LiDAR）作为车辆环境感知的关键传感器，其采集的点云数据的准确性是实现车辆对三维环境理解的关键。但在虚拟环境中，通过3D渲染技术模拟的点云数据难以真实反映传感器在复杂工况下的变化规律。本文提出一种用于自动驾驶仿真的虚拟LiDAR传感器建模方法。该方法首先基于Unity 3D引擎构建LiDAR的几何测量模型。其次，结合真实传感器的衰变特性推导简化的LiDAR物理模型。最后，基于蒙特卡罗方法在随机模型上对仿真数据进行噪声模拟，从而实现高保真的LiDAR数据输出。所提出的方法可结合精细化的虚拟场景进行数据验证，实验结果表明：该方法能够有效地在虚拟环境下模拟LiDAR数据，从而应用于自动驾驶仿真算法验证过程。     

Multi-scale simulation model of air system based on cross-dimensional data transmission method

The Secondary Air System (SAS) plays an important role in the safe operation and performance of aeroengines. The traditional 1D-3D coupling method loses information when used for secondary air systems, which affects the calculation accuracy. In this paper, a Cross-dimensional Data Transmission method (CDT) from 3D to 1D is proposed by introducing flow field uniformity into the data transmission. First, a uniformity index was established to quantify the flow field parameter distribution characteristics, and a uniformity index prediction model based on the locally weighted regression method (Lowess) was established to quickly obtain the flow field information. Then, an information selection criterion in 3D to 1D data transmission was established based on the Spearman rank correlation coefficient between the uniformity index and the accuracy of coupling calculation, and the calculation method was automatically determined according to the established criterion. Finally, a modified function was obtained by fitting the ratio of the 3D mass-average parameters to the analytical solution, which are then used to modify the selected parameters at the 1D-3D interface. Taking a typical disk cavity air system as an example, the results show that the calculation accuracy of the CDT method is greatly improved by a relative 53.88% compared with the traditional 1D-3D coupling method. Furthermore, the CDT method achieves a speedup of 2 to 3 orders of magnitude compared to the 3D calculation.