阴影模型的正则化无设备重建与实时定位

熊一枫; 卢继华; 何梓珮; 曹晨曦

doi:10.16383/j.aas.2015.c130441

阴影模型的正则化无设备重建与实时定位

doi: 10.16383/j.aas.2015.c130441

1.
北京理工大学信息与电子学院北京 100081

基金项目:

国家高技术研究发展计划(863计划) (2012AA121604), 国家自然科学基金(61002014, 61101129, 61227001, 61072050)资助

详细信息

作者简介:
熊一枫北京理工大学信息与电子学院本科生. 主要研究方向为无线层析成像,协作通信, 数字图像处理.E-mail: xyfefron@126.com

通讯作者:
卢继华博士, 北京理工大学信息与电子学院讲师. 主要研究方向为无线层析成像, 协作通信, 物理层安全通信,MIMO 信道建模. E-mail: lujihua@bit.edu.cn

计量
- 文章访问数: 1463
- HTML全文浏览量: 77
- PDF下载量: 920
- 被引次数: 0
出版历程
- 收稿日期: 2013-06-05
- 修回日期: 2014-05-15
- 刊出日期: 2015-06-20

Device-free Reconstruction and Real-time Location Based on Shadowing Model in Radio Tomographic Imaging

1.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081

Funds:

Supported by National High Technology Research and Development Program of China (863 Program) (2012AA121604), and National Natural Science Foundation of China (61002014, 61101 129, 61227001, 61072050)

摘要

摘要: 在综合静态无线射频层析成像(Radio tomographic imaging, RTI)算法基础上, 给出了一种可行且有效的实现无线传感器节点在空旷环境和障碍物条件下无线信号衰减原理障碍物监控的方法,实现定位与追踪.利用阴影衰落模型建立接收信号强度测量值线性系统模型,并采用SPIN令牌环通信协议收集接收信号强度;创新性地引入最小角回归算法与最小绝对值收缩和选择因子算法(Least absolute shrinkage and selection operator, LASSO), 提高了图像重建速度. 即在吉洪诺夫正则化与l1正则化算法分析对比前提下,创新性引入改进的最小角回归(Least angle regression, LARS) 重建模型与算法,保证重建效果与复杂LASSO算法相似的同时,将重建图像速度提高一个数量级. 实测基于16平方米范围内的16个JENNIC 5139节点进行定位与追踪.实测结果与仿真相比虽稍有偏差,但近似符合. 这充分表明:吉洪诺夫正则化与l1正则化适用于不同分辨率场景,且都可较好地反映障碍物状况.
- 无线射频层析成像 /
- 重建算法 /
- 最小角回归算法 /
- l1最优化 /
- 吉洪诺夫正则化
Abstract: The emerging technology, radio tomographic imaging (RTI), uses the attenuation characteristic of wireless signal to locate and trace objects. A linear model with the SPIN communication protocol for received signal strength (RSS) measurements is presented in this paper to get objects' image in our deployed RTI system. To improve the image reconstruction speed, least absolute shrinkage and selection operator (LASSO) algorithm of compressed sensing field is referred to and compared. Moreover, modified l1-norm regularization is adopted to enhance the resolution of image reconstruction, which is compared with Tikhonov. Based on sixteen JENNIC 5139 sensor nodes, some experiments have been developed for imaging and tracking the objects inside an area of sixteen square feet. Although there are some differences between simulations and real experiments, the positions of objects can be accurately located from both simulations and real measurements.
- Radio tomographic imaging (RTI) /
- reconstruction algorithm /
- least angle regression /
- l1-optimization /
- Tikhonov

HTML全文

参考文献(20)

[1]	Patwari N, Wilson J. RF sensor networks for device-free localization: measurements, models, and algorithms. Proceedings of the IEEE, 2010, 98(11): 1961-1973
[2]	[2] Wilson J, Patwari N. Radio tomographic imaging with wireless networks. IEEE Transactions on Mobile Computing, 2010, 9(5): 621-632
[3]	[3] Wilson J, Patwari N. See-through walls: motion tracking using variance-based radio tomography networks. IEEE Transactions on Mobile Computing, 2011, 10(5): 612-621
[4]	[4] Chen X, Edelstein A, Li Y P, Coates M, Rabbat M, Men A. Sequential Monte Carlo for simultaneous passive device-free tracking and sensor localization using received signal strength measurements. In: Proceedings of the 10th International Conference on Information Processing in Sensor Networks (IPSN). Chicago, USA: IEEE, 2011. 342-353
[5]	[5] Kaltiokallio O, Bocca M. Real-time intrusion detection and tracking in indoor environment through distributed RSSI processing. In: Proceedings of the 17th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). Toyama, Japan: IEEE, 2011. 61-70
[6]	[6] Wilson J, Patwari N. A fade-level skew-Laplace signal strength model for device-free localization with wireless networks. IEEE Transactions on Mobile Computing, 2012, 11(6): 947-958
[7]	[7] Patwari N, Agrawal P. Effects of correlated shadowing: connectivity, localization, and RF tomography. In: Proceedings of the 2008 International Conference on Information Processing in Sensor Networks. St. Louis, Missouri, USA: IEEE 2008. 82-93
[8]	[8] Yanovsky F J, Ivashchuk V E, Prokhorenko V P. Through-the-wall surveillance technologies. In: Proceedings of the 6th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS). Sevastopol, Peninsula: IEEE, 2012. 30-33
[9]	[9] Zhao Y, Patwari N, Phillips J M, Venkatasubramanian S. Radio tomographic imaging and tracking of stationary and moving people via kernel distance. In: Proceedings of the 12th International Conference on Information Processing in Sensor Networks (IPSN'13). Philadelphia, Pennsylvania, USA: ACM, 2013. 229-240
[10]	Haimovich A M, Blum R S, Cimini L J. MIMO radar with widely separated antennas. IEEE Signal Processing Magazine, 2008, 25(1): 116-129
[11]	Youssef M, Mah M, Agrawala A. Challenges: device-free passive localization for wireless environments. In: Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking. Montreal, QC, Canada: ACM, 2007. 222-229
[12]	Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666
[13]	Kanso M A, Rabbat M G. Compressed RF tomography for wireless sensor networks: centralized and decentralized approaches. In: Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS'10). Marina del Rey, CA, USA: IEEE, 2009. 173- 186
[14]	Kaltiokallio O, Bocca M, Patwari N. Enhancing the accuracy of radio tomographic imaging using channel diversity. In: Proceedings of the 9th International Conference on Mobile Adhoc and Sensor Systems (MASS), Las Vegas, Nevada, USA: IEEE, 2012. 254-262
[15]	Wilson J, Patwari N. Regularization methods for radio tomographic imaging. In: Proceedings of the 2009 Virginia Technical Symposium on Wireless Personal Communications. Blacksburg, VA, USA: VA Tech, 2009. 1-9
[16]	An Yao-Zu, Lu Yao, Zhao Hong. An adaptive-regularized image super-resolution. Acta Automatica Sinica, 2012, 38(4): 601-608 (安耀祖, 陆耀, 赵红. 一种自适应正则化的图像超分辨率算法. 自动化学报, 2012, 38(4): 601-608)
[17]	Xu Zong-Ben, Guo Hai-Liang, Wang Yao, Zhang Hai. Representative of L1/2 regularization among Lq (0Acta Automatica Sinica, 2012, 38(7): 1225-1228
[18]	Yi Lian-Jie. Radio Tomographic Imaging Governed by Compressed Sensing and Its Application [Master dissertation], Sun Yat-Sen University, China, 2010.(义连杰. 压缩传感支配的无线电层析成像及其应用 [硕士学位论文], 中山大学, 中国, 2010.)
[19]	Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression. The Annals of Statistics, 2004, 32(2): 407-499
[20]	Wu T T. LASSO penalized semiparametric regression on high-dimensional recurrent event data via coordinate descent. Journal of Statistical Computation and Simulation, 2013, 83(6): 1145-1155

施引文献

资源附件(0)

访问统计

计量

文章访问数: 1463
HTML全文浏览量: 77
PDF下载量: 920
被引次数: 0

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

阴影模型的正则化无设备重建与实时定位

doi: 10.16383/j.aas.2015.c130441

作者简介:
熊一枫北京理工大学信息与电子学院本科生. 主要研究方向为无线层析成像,协作通信, 数字图像处理.E-mail: xyfefron@126.com

通讯作者:
卢继华博士, 北京理工大学信息与电子学院讲师. 主要研究方向为无线层析成像, 协作通信, 物理层安全通信,MIMO 信道建模. E-mail: lujihua@bit.edu.cn

计量

Device-free Reconstruction and Real-time Location Based on Shadowing Model in Radio Tomographic Imaging

计量

目录

留言板

阴影模型的正则化无设备重建与实时定位

doi: 10.16383/j.aas.2015.c130441

作者简介: 熊一枫 北京理工大学信息与电子学院本科生. 主要研究方向为无线层析成像,协作通信, 数字图像处理.E-mail: xyfefron@126.com

通讯作者: 卢继华 博士, 北京理工大学信息与电子学院讲师. 主要研究方向为无线层析成像, 协作通信, 物理层安全通信,MIMO 信道建模. E-mail: lujihua@bit.edu.cn

计量

出版历程

Device-free Reconstruction and Real-time Location Based on Shadowing Model in Radio Tomographic Imaging

计量

出版历程

目录

作者简介:
熊一枫北京理工大学信息与电子学院本科生. 主要研究方向为无线层析成像,协作通信, 数字图像处理.E-mail: xyfefron@126.com

通讯作者:
卢继华博士, 北京理工大学信息与电子学院讲师. 主要研究方向为无线层析成像, 协作通信, 物理层安全通信,MIMO 信道建模. E-mail: lujihua@bit.edu.cn