增强型超声波周向旋转扫描节点参数优化

doi:10.13229/j.cnki.jdxbgxb201701038

摘要/Abstract

摘要： 无线传感器网络(WSN)节点具有的全向感知能力决定了网络覆盖性能,故先对增强型超声波传感器的尺寸和外形进行了优化仿真。仿真结果表明:优化后的号筒对超声波发射声场的指向性提升较为明显,在中心轴线上辐射的声压比普通传感器大3.5倍。然后利用增强型超声波传感器设计了单发单收超声波旋转扫描系统,建立了针对超声波测距的最高扫描转速模型,确定了扫描转速与超声波脉冲频率的匹配关系,并分析了该系统的目标捕获率。计算结果表明:超声波旋转扫描系统在具有较好匹配度的情况下,最高转速为215 r/min,可捕获速度低于64.5 km/h的目标。

关键词: 计算机应用, 无线传感器网络, 增强型超声波, 旋转扫描, 目标捕获

Abstract: Directional Sensor Network (DSN) node with stability of omnidirectional perception plays a decisive role in network coverage. Using the optimization method of SNOPT, the dimensions and shape of enhanced ultrasonic transducer were optimized first. With optimized horn the directivity was promoted obviously and the radiated sound pressure in the central axis was 3.5 times bigger than that of normal transducer. Then a new rotational circumferential scanning program of single-beam was designed based on the enhanced ultrasonic transducer. The model of the highest scanning speed for ultrasonic ranging was built, and the highest scanning speed of 253 r/min was achieved for monitoring the target in 6 m away. In order to get stabilized prospecting effect, the matching relationship between scanning speed and pulse frequency was established for constant number of ultrasonic beams in one scanning period. At last, target acquisition rate was analyzed. Calculation results demonstrate that the highest speed of the ultrasonic circumferential scanning system is 215 r/min in the case of good compatibility, which can acquire the target with a speed lower than 65.5 km/h.

Key words: computer application, wireless sensor network(WSN), enhanced ultrasonic, rotational scanning, target acquisition

中图分类号:

TP212

朱海洋, 张合, 马少杰. 增强型超声波周向旋转扫描节点参数优化[J]. 吉林大学学报(工学版), 2017, 47(1): 262-267.

ZHU Hai-yang, ZHANG He, MA Shao-jie. Parameter optimization of enhanced ultrasonic circumferential scanning node in WSN[J]. 吉林大学学报(工学版), 2017, 47(1): 262-267.

参考文献

[1] 赵静,曾建湖. 无线多媒体传感器网络感知模型与数量估计[J]. 软件学报, 2012, 23(8): 2104-2114.
Zhao Jing, Zeng Jian-hu. Sense model and number estimation of wireless multimedia sensor networks[J]. Journal of Software, 2012, 23(8): 2104-2114.
[2] 董利达,黄聪,管林波. 基于双树结构的无线HART调度策略[J]. 浙江大学学报:工学版, 2014, 48(3):391-397.
Dong Li-da, Huang Cong, Guan Lin-bo. Double-tree structure based scheduling strategy for wireless HART[J]. Journal of Zhejiang University(Engineering Science), 2014, 48(3):391-397.
[3] Hossain A, Biswas P K, Chakrabarti S. Sensing models and its impact on network coverage in wireless sensor network[C]∥IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, India, 2008.
[4] 蒋一波,王万良,陈伟杰,等. 视频传感器网络中无盲区监视优化[J]. 软件学报, 2012, 23(2):310-322.
Jiang Yi-bo, Wang Wan-liang, Chen Wei-jie, et al. Coverage optimization of occlusion-free surveillance for video sensor networks[J]. Journal of Software, 2012, 23(2):310-322.
[5] 胡小青,胥布工,刘永桂,等.分布式的有向传感器网络目标跟踪算法[J].华南理工大学学报:自然科学版,2013,41(9):8-14.
Hu Xiao-qing,Xu Bu-gong,Liu Yong-Gui,et al.Distributed target tracking algorithm in directional sensor networks[J].Journal of South China University of Technology(Natural Science Edition),2013,41(9):8-14.
[6] 杨波,邹富强. 异向性磁阻传感器检测车流量的新方法[J].浙江大学学报:工学版,2011,45(12):2109-2114.
Yang Bo,Zou Fu-qiang.A novel approach for vehicle flow detection by using anisotropic magnetoresistive sensor[J].Journal of Zhejiang University(Engineering Science),2011,45(12):2109-2114.
[7] 李元实,王智,鲍明,等. 基于无线声阵列传感器网络的实时多目标跟踪平台设计及实验[J]. 仪器仪表学报, 2012, 33(1): 146-153.
Li Yuan-Shi, Wang Zhi, Bao Ming, et al. Design and experiment for real time multi-target tracking platform based on wireless acoustic array sensor networks[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 146-153.
[8] 朱海洋,张合,马少杰. 低功耗声/超声复合传感器节点设计[J].仪器仪表学报,2014,35(10): 2223-2230.
Zhu Hai-yang, Zhang He, Ma Shao-jie. Design of low power acoustic-ultrasonic compound sensor node[J]. Chinese Journal of Scientific Instrument, 2014, 35(10): 2223-2230.
[9] Li H B, Miao D, Chen J M, et al. Networked ultrasonic sensors for target tracking:an experimental study[C]∥Global Telecommunications Conference, Honolulu, Hawaii, USA, 2009.
[10] 查冰婷,张合. 单光束脉冲激光引信扫描频率和脉冲频率研究[J].红外与激光工程,2014,43(7):2081-2086.
Zha Bing-ting,Zhang He. Scanning frequency and pulse frequency of single-beam pulsed laser fuze[J].Infrared and Laser Engineering,2014,43(7):2081-2086.
[11] Wang W H, He Y, Li Z J, et al. A real-time target detection algorithm for infrared search and track system based on ROI extraction[C]∥Signal Processing, Communication and Computing(ICSPCC), Hong Kong,China, 2012:774-778.
[12] 潘仲明,贺汉根. 超声阵列障碍探测技术及其在无人车辆中的应用[J]. 国防科技大学学报, 2009, 31(2):121-125.
Pan Zhong-ming, He Han-gen. Application of ultrasonic transducer array for obstacle detection technology in unmanned automobile[J]. Journal of National University of Defense Technology, 2009, 31(2):121-125.
[13] Bängtsson E, Noreland D, Berggren M. Shape optimization of an acoustic horn[J]. Computer Methods in Applied Mechanics & Engineering,2003, 192(11-12):1533-1571.
[14] 李晖, 孙伟, 许卓, 等. 基于激光旋转扫描的约束态薄壁圆柱壳模态阵型测试新方法[J]. 振动与冲击, 2014, 33(16): 155-159.
Li Hui, Sun Wei, Xu Zhuo, et al. Experimental method of laser rotating scanning to measure mode shapes of constrained thin cylindrical shell[J]. Journal of Vibration and Shock, 2014, 33(16): 155-159.
[15] 朱金台,董晓龙,林文明,等. Ku波段旋转扫描扇形波束散射计地面扩展目标在轨定标[J]. 电子与信息学报,2013, 35(8): 1793-1799.
Zhu Jin-tai, Dong Xiao-long, Lin Wen-ming, et al. Calibration of the Ku-band rotating fan-beam scatterometer using land extended-area targets[J]. Journal of Electronics & Information Technology, 2013, 35(8): 1793-1799.

相关文章 15

[1]	刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850.
[2]	王利民,刘洋,孙铭会,李美慧. 基于Markov blanket的无约束型K阶贝叶斯集成分类模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1851-1858.
[3]	金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866.
[4]	赵东,孙明玉,朱金龙,于繁华,刘光洁,陈慧灵. 结合粒子群和单纯形的改进飞蛾优化算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1867-1872.
[5]	刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[6]	欧阳丹彤, 范琪. 子句级别语境感知的开放信息抽取方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1563-1570.
[7]	刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599.
[8]	桂春, 黄旺星. 基于改进的标签传播算法的网络聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1600-1605.
[9]	刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.
[10]	车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.
[11]	赵宏伟, 刘宇琦, 董立岩, 王玉, 刘陪. 智能交通混合动态路径优化算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12]	黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13]	傅文博, 张杰, 陈永乐. 物联网环境下抵抗路由欺骗攻击的网络拓扑发现算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14]	曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15]	侯永宏, 王利伟, 邢家明. 基于HTTP的动态自适应流媒体传输算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1244-1253.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed