小波分析在光纤陀螺寻北中的应用

对某型光纤陀螺仪的实测信号进行了分析。为了降低噪声对光纤陀螺寻北的影响,提高寻北精度,利用五点三次平滑、数字滤波、小波变换分析法对光纤陀螺静态输出数据进行了滤波处理,通过对比滤波前后陀螺的输出曲线、标准差以及频谱图,可以发现：小波阈值滤波能够有效的补偿光纤陀螺的随机误差。通过小波分析可以显示出陀螺低频信号的发展趋势。利用去噪后的数据进行二位置寻北计算,结果表明：小波分析可以使精度提高29．8％。     

基于前向线性预测和小波的FOG信号降噪算法研究

针对光纤陀螺输出信号的噪声,提出了一种处理该噪声的前向线性预测滤波与小波变换相结合的级联滤波方法,以前向线性预测滤波作为前段滤波器,采用DB4小波函数的强制阈值小波变换作为第二级滤波器。运用Allan方差法对级联滤波结果进行了分析,结果表明该级联滤波能取得较好的光纤陀螺信号降噪效果。     

船舶MEMS微惯导网络精度技术研究

为了减少误差积累,提高导航精度,通过船舶上的CAN总线网络,利用高精度主惯导系统对低精度的MEMS微惯导系统进行在线修正。根据微惯导网络系统姿态角的误差模型,将惯导系统的角速率输出值作为量测信息设计卡尔曼滤波器,对姿态角修正算法进行了仿真运算,估计出了MEMS微惯导系统姿态角误差。     

改进遗传粒子滤波在弹道重构中的应用

为了有效地进行弹道重构,提出了将改进遗传算法应用于粒子滤波的重构方法。首先分析了弹道发射及量测过程,明确了主要误差源;然后根据选用的质点弹道模型,采用Matlab／sIMILINK进行了弹道建模,实现了对系统简单准确的描述;再次,提出采用改进的遗传粒子滤波方法进行弹道重构,估计弹道参数,在遗传操作中,为了不断产生适应值更大的个体,引入了混沌变异算子;最后进行了仿真计算,仿真结果表明,弹道重构结果精度较高,与实际模型更加接近。     

基于传统CIC抽样滤波器的改进与仿真

文章分析了传统CIC（Cascade Intergrator-Comb）滤波器幅频响应不理想的原因,并针对其问题提出了分别用级联余弦滤波器以及增益补偿滤波器来改善其性能。仿真结果表明,改进后的CIC滤波器阻带衰减有26dB的提高,且通带特性有良好的改善。最后其实现结构特性表明,改进后的CIC滤波器在实际应用和深入研究中有着现实意义。     

灰分沉积对不同结构CDPF再生特性的影响

对某高压共轨柴油机的催化型颗粒物捕集器(CDPF)进行台架试验,研究了两种不同结构的CDPF对发动机性能的影响和不同碳载量下CDPF的压降特性。通过建立三维CDPF热力学模型,分析了灰分量和灰分分布系数对不同结构CDPF再生特性的影响。结果表明:CDPF会使柴油机动力性、经济性略有下降,但非对称结构CDPF可有效降低载体对柴油机性能的影响;CDPF压降会随载体孔道内碳烟累积量的增大而升高,非对称结构CDPF可有效减小CDPF压降,且随着碳载量增加,非对称结构的优势更加明显。灰分沉积有利于降低载体向环境传热的导热率,提高载体热容量,有利于碳烟的氧化再生;CDPF再生过程中载体的温度呈边缘低中心高、前端低后端高的分布规律。灰分分布系数对碳烟氧化速率影响较小,载体结构、灰分量和灰分分布系数对CDPF最大再生温度梯度影响也不大。     

基于组合定位的现代有轨电车雷达防撞系统设计

现代有轨电车作为一种新型公共交通工具,因半独立路权的运营方式使其与其他社会车辆相撞的事故近年来频频发生,有轨电车防撞系统成为保障现代有轨电车安全运营的重要设备。在分析比较传统防撞系统的方式方法后,根据现代有轨电车实际运营环境特征,结合卫星导航与无线射频识别技术,提出一种基于扩展卡尔曼滤波和目标跟踪算法的低成本组合定位雷达防撞系统。测试结果表明,该防撞系统在结合组合定位模块数据后可有效判断列车当前行驶的危险区域范围,降低单一雷达防撞系统的误报警率,及时发出报警信息,提高防撞系统的准确性和稳定性,更好的保障有轨电车运行安全。     

Efficient Recognition of Human Faces from Video in Particle Filter

Introduction As an active research in computer vision andimage understanding, face recognition from videohas got wide applications, such as human-comput-er interface, video surveillance, ATM and videocommunications[1]. So far, there are many litera-tures on face recognition from video. Many archi-tectures about dynamic face recognition were pro-posed in those literatures. Tracking and recogni-tion were performed separately in Ref.[2]. Thelimitation in this method is that tracking andrecognit…     

Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNN_reg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNN_rec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNN_rec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R²), followed by the GCNN_reg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the “black box” of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.     

隧道交通事故自动检测仿真研究

采用VISSIM交通仿真软件,对城市隧道内的物理环境进行仿真,可设计出一种基于交通流参数的交通事故自动检测方法。该方法可以方便、快捷、准确地判断隧道内的交通运行状况,因而具有一定的实用价值。