时变微弱扩频信号检测

提出了一种解决微弱高动态扩频信号捕获的新方法,利用分数阶域滤波设计了伪码—时频联合匹配滤波的框架,最后进行了相关计算并给出了应用实例。     

磁暴期间的地磁导航精度分析

地磁导航是无源自主导航技术研究的新方向. 分析了地磁导航的基本原理, 描述了典型磁暴过程, 并针对地磁导航在磁暴环境中的适用性进行了研究. 在采用曲面样条方法对实测地磁场数据建立观测模型的基础上, 结合广义卡尔曼滤波方法讨论了磁暴不同阶段对地磁导航精度造成的影响. 分别采用理论典型磁暴数据以及实测磁暴数据进行仿真, 仿真结果表明, 在磁暴的初相、恢复相的中后时段以及中等强度以下的磁暴全过程仍然可以采用地磁来进行导航定位, 导航精度在200 m以内, 满足飞行器中程制导的精度要求.      

Filters for manufacturability in design optimization of variable stiffness composites

In the design optimization of variable stiffness composites, manufacturing constraints imposed by the automated fiber placement technology must be considered. In the present paper, two filters are proposed to address this issue, and they are incorporated into the Shepard interpolation-based design optimization framework developed in our previous studies. The fiber angle arrangement of a composite is represented by a continuous function that interpolates fiber angles at scattered design points. Two filters are appointed for each design point to deal with two typical manufacturing constraints, i.e., fiber curvature and gap/overlap. At each design point, the sensitivity is first filtered in a rectangular region around this point, and by this means the fiber curvature is controlled; then in another rectangular region around this design point, the filtered sensitivities are averaged, and the result is used to update the corresponding design variable. Several numerical examples are investigated, and the results show that the proposed method is effective.     

集中空调新风系统常见故障及排除方法

通过对空调新风系统重要性的分析,指出了影响集中空调系统新风量和新风品质的原因,提出了运用预埋法兰连接风管来保证新风量,在通风井底部设检查口清理新风竖井保证新风品质。     

基于局部平稳AR模型的信号结构变化点精确估计方法

研究了局部平稳AR模型,提出了基于递归辅助变量（RIV）双格型滤波算法,通过统计分析信息量AIC的变化精确估计信号结构变化点。对遥测速变参数的分析表明,该方法能够精确定位信号结构的变化点,实现局部平稳信号的精确分割。     

无人机蜂群轨迹预测研究

传统防空火控算法中的轨迹预测模型无法对复杂的无人机蜂群进行有效地轨迹预测，而现有针对无人机机动轨迹的预测研究通常只考虑单个无人机，且模型量级过大。为了准确且快速地预测无人机蜂群轨迹，本文提出一种面向蜂群的轨迹预测方法。在获得蜂群轨迹后，首先基于DBSCAN 对其进行聚类，判断出蜂群中各个无人机的类别；然后基于分形算法，判断无人机轨迹是简单轨迹还是复杂轨迹；最后，采用卡尔曼滤波进行简单轨迹的预测，用基于LSTM 网络的方法进行复杂轨迹的预测。结果表明：本文提出的无人机蜂群轨迹预测方法的预测误差远远小于纯采用卡尔曼滤波方法预测的误差，且预测时间小于仅采用LSTM 网络方法预测的时间，可以较为准确地预测蜂群中不同集群无人机的轨迹，为反无人机蜂群火控解算提供基础。     

De-combination of belief function based on optimization

In the theory of belief functions, the evidence combination is a kind of decision-level information fusion. Given two or more Basic Belief Assignments(BBAs) originated from different information sources, the combination rule is used to combine them to expect a better decision result. When only a combined BBA is given and original BBAs are discarded, if one wants to analyze the difference between the information sources, evidence de-combination is needed to determine the original BBAs. Evidence d...     

Periodic acoustic source tracking using propagation delayed measurements

There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measuremen...     

Measurement-driven Gauss-Hermite particle filter with soft spatiotemporal constraints for multi-optical theodolites target tracking

Multi-Optical Theodolite Tracking systems (MOTTs) can stealthily extract the target’s status information from bearings only through non-contact measurement. The constrained MOTTs are partially compatible, yet many existing research works and results are based on the known model, ignoring its discrimination with the target maneuvering behavior pattern. To compensate for these mismatches, this paper develops a Measurement-driven Gauss-Hermite Particle Filter (MGHPF), which elegantly fuses the spatiotemporal constraints and its soft form to perform MOTT missions. Specifically, the target dynamic model and tracking algorithm are based on the target behavior pattern with the adaptive turn rate, fully exploiting the spatial epipolar geometry characteristics for each intersection measurement by a minimax strategy. Then, the center of the feasible area is approximated via the analytic coordinate transformation, and the latent samples are updated via the deterministic Gauss-Hermite integral method with the target’s predictive turn rate. Simultaneously, the effects of truncation correction and compensation feedback from the current measurement and historical estimation data are adaptively incorporated into the PF’s importance distribution to cover the mixture likelihood. Besides, an effective causality-invariant updating rule is provided to estimate the parameters of these soft spatiotemporal constrained MOTTs with convergence guarantees. Simulated and measured results show good agreement; compared with the state-of-the-art Multi-Model Rao-Blackwell Particle Filter (MMRBPF), the proposed MGHPF improves the filtering accuracy by 7.4%-34.7% and significantly reduces the computational load.     

基于DKF的IMU误差预测算法

作为导航领域常用的组合导航方式,全球导航卫星系统(GNSS)/惯性导航系统(INS)组合导航在GNSS信号失锁后,由于惯性测量单元(IMU)误差随时间迅速积累,其定位结果会偏离载体真实位置,导航精度下降.针对此问题,提出了一种长短期记忆网络(LSTM)辅助的算法,称之为深度卡尔曼滤波(DKF)算法.DKF算法的核心思想是使用LSTM训练IMU误差模型,然后通过训练出的模型预测IMU误差,最后将预测的IMU误差代入IMU数据以校正导航结果.仿真结果表明:在200s测试数据上,DKF算法将误差从1.1537m/s降低到0.3746m/s.与平均预测、卡尔曼预测和最小二乘估计等方法相比,DKF算法的误差最小,具有更优越的导航性能.