十字交叉排列面波压制方法及应用

考虑面波特征和古龙断陷区地震资料面波强的特点,提出面波压制的十字交叉排列法.该方法将地震资料中的炮点线和检波点线重排后,进行三维Fourier变换;在频率—波数域滤波后,进行Fourier反变换,实现面波压制处理.古龙断陷区地震资料面波压制结果表明,十字交叉排列面波压制方法具有很好的保幅性和实用性,能够为地震资料保幅处理提供较好的叠前预处理方法,可以在深层地震资料处理中推广应用.

华北地区GPS基准站坐标时间序列噪声特征研究

利用华北地区GPS连续运行基准站网络2008-08~2013-04观测数据,分析36个基准站坐标时间序列的噪声特征。利用区域堆栈滤波方法对GPS单日解坐标时间序列进行共性误差剔除,使用极大似然估计准则定量估计坐标时间序列的噪声特性,并分析有色噪声对测站计算速度的影响。结果表明,华北地区GPS基准站坐标时间序列的共性误差在N、E和U方向分别约为1mm、1mm和3mm,且共性误差具有类似闪烁噪声的特性;共性误差剔除前,坐标时间序列的噪声特性可以用可变白噪声加闪烁噪声模型或可变白噪声加幂律噪声模型来描述;共性误差剔除后降低了坐标时间序列噪声中闪烁噪声的成分,突出了本地效应部分噪声,坐标时间序列的噪声特性可以用可变白噪声加闪烁噪声和随机漫步噪声模型或可变白噪声加幂律噪声模型来描述;最优噪声模型计算的速度误差比仅考虑可变白噪声所计算的速度误差增大5~8倍;剔除共性误差,可使测站速度的精度获得40%左右的提高。

Robot SLAM with Ad hoc wireless network adapted to search and rescue environments

An innovative multi-robot simultaneous localization and mapping(SLAM)is proposed based on a mobile Ad hoc local wireless sensor network(Ad-WSN).Multiple followed-robots equipped with the wireless link RS232/485module act as mobile nodes,with various on-board sensors,Tp-link wireless local area network cards,and Tp-link wireless routers.The master robot with embedded industrial PC and a complete robot control system autonomously performs the SLAM task by exchanging information with multiple followed-robots by using this self-organizing mobile wireless network.The PC on the remote console can monitor multi-robot SLAM on-site and provide direct motion control of the robots.This mobile Ad-WSN complements an environment devoid of usual GPS signals for the robots performing SLAM task in search and rescue environments.In post-disaster areas,the network is usually absent or variable and the site scene is cluttered with obstacles.To adapt to such harsh situations,the proposed self-organizing mobile Ad-WSN enables robots to complete the SLAM process while improving the performances of object of interest identification and exploration area coverage.The information of localization and mapping can communicate freely among multiple robots and remote PC control center via this mobile Ad-WSN.Therefore,the autonomous master robot runs SLAM algorithms while exchanging information with multiple followed-robots and with the remote PC control center via this local WSN environment.Simulations and experiments validate the improved performances of the exploration area coverage,object marked,and loop closure,which are adapted to search and rescue post-disaster cluttered environments.

An efficient human detection method for multi-pedestrian tracking

Traditional human detection using pre-trained detectors tends to be computationally intensive for time-critical tracking tasks, and the detection rate is prone to be unsatisfying when occlusion, motion blur and body deformation occur frequently. A spatial-confidential proposal filtering method(SCPF) is proposed for efficient and accurate human detection. It consists of two filtering phases: spatial proposal filtering and confidential proposal filtering. A compact spatial proposal is generated in the first phase to minimize the search space to reduce the computation cost. The human detector only estimates the confidence scores of the candidate search regions accepted by the spatial proposal instead of global scanning. At the second phase, each candidate search region is assigned with a supplementary confidence score according to their reliability estimated by the confidential proposal to reduce missing detections. The performance of the SCPF method is verified by extensive tests on several video sequences from available public datasets. Both quantitatively and qualitatively experimental results indicate that the proposed method can highly improve the efficiency and the accuracy of human detection.

基于多模特征的足迹识别算法

基于足迹压力数据提出一种基于多模特征足迹识别算法.该算法采用连通区域滤波法实现足迹压力数据的去噪,对去噪后的数据提取足迹图像的形态特征、压力特征及卷积特征,并基于各特征权重实现足迹多特征的优化融合,最后采用支持向量机(SVM)分类器进行分类识别.实验结果表明:在50人的足迹压力数据上,三类不同模态足迹的识别准确率分别达到了100%,99.925%和94.445%,相较于仅采用形态特征和压力特征的识别,所提出算法的平均识别率提高了10.285%,表明该识别算法能够有效进行足迹识别.