基于分层滤波算法的无人机控制系统故障检测技术

针对传统无人机控制系统故障检测技术检测误差率高,检测速率低的问题,引入分层滤波算法研究了一种新的无人机控制系统故障检测技术,构建地面坐标系得到模型数据,同时收集无人机系统的内部转向角因素,录入转向角数据,采用算法整合处理主系统数据,利用滤波手段控制数据录入量,综合提取特征参数,选取不同于正常状态的特征参数,利用噪声估计器诊断故障,分析残差与“零”之间的关系,从而实现无人机控制系统的故障检测;实验结果表明,相较于传统技术,基于分层滤波算法的无人机控制系统故障检测技术误差率降低了0.56%,检测速率提升了27.39%。

距离图像的分层滤波处理算法

距离图像是指由波束扫描和脉冲反射测距相结合形成的图像,它与常规的光学图像相比具有一些不同的统计特性.提出一种用于处理这种距离图像的分层滤波处理算法,它利用了距离图像中存在多目标时统计直方图具有多峰值的特点进行分层分割、分别滤波,再合成重建新图像.与常规的滤波处理方法相比,它能更加有效地滤除噪声干扰,获得清晰的目标和背景图像.

基于CUDA的塔台模拟机冲突检测算法

塔台模拟机冲突检测算法是一种耗时大的并行算法。针对其导致塔台模拟系统核心服务器CPU负担过重的缺点,在常用冲突检测算法的基础上,提出一种基于统一设备构架(CUDA)的塔台模拟机冲突检测实现方案。首先介绍GPU并行运算的体系结构基础,并将基于卡尔曼滤波的目标物体跟踪技术的分层冲突检测算法移植到GPU。然后利用相同价格的CPU和GPU对比运算效果。实验结果表明:与相同算法的CPU实现方案相比,GPU实现方案将计算效率提高10～50倍。使用此方案,极大地减轻了核心服务器的负担,使塔台模拟机的性能得到质的提高。

Exploring on Hierarchical Kalman Filtering Fusion Accuracy

Aim To analyze the traditional hierarchical Kalman filtering fusion algorithm theoretically and point out that the traditional Kalman filtering fusion algorithm is complex and can not improve the tracking precision well, even it is impractical, and to propose the weighting average fusion algorithm. Methods The theoretical analysis and Monte Carlo simulation methods were ed to compare the traditional fusion algorithm with the new one,and the comparison of the root mean square error statistics values of the two algorithms was made. Results The hierarchical fusion algorithm is not better than the weighting average fusion and feedback weighting average algorithm The weighting filtering fusion algorithm is simple in principle, less in data, faster in processing and better in tolerance.Conclusion The weighting hierarchical fusion algorithm is suitable for the defective sensors.The feedback of the fusion result to the single sersor can enhance the single sensorr's precision. especially once one sensor has great deviation and low accuracy or has some deviation of sample period and is asynchronous to other sensors.