基于无线传感器网络的室内定位技术研究
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摘要
近年来,室内定位技术在大型场馆人员定位、仓储物流、矿山井下人员定位、应急救援等复杂环境下的人员与物品定位应用中发挥着极其重要的作用。基于无线传感器网络的室内定位技术凭借其传感器节点低成本、低功耗、易于组网数据传输等特性具有独特的优势。论文主要针对如何提高无线传感器网络室内定位精度进行了以下研究。首先,对无线传感器网络室内定位系统组成及节点组网协议进行了详细分析。分析研究了无线传感器网络中基于测距和非测距的定位原理,并对质心、DV-Hop算法的定位性能进行了仿真分析。
其次,研究了RSSI-d衰减特性,对室内信号模型进行了分析,确定了算法所用室内信号路径损耗模型;针对RSSI方法测距定位误差较大的问题,提出了基于RSSI测距误差修正的加权质心定位算法。算法测距阶段采用基于最小二乘测距误差补偿方法对RSSI测距进行修正,以减小测距误差。定位阶段采用改进Euclidean加权质心定位算法,用信标节点对未知节点的不同影响力来确定加权因子,以提高定位精度。仿真表明,该算法精度较其它加权质心定位算法有了明显提高。
最后,针对最小二乘法计算未知节点估计位置与实际位置偏差大的问题,提出了基于WLS与EKF的混合定位算法(CLA)。该算法在RSSI测距误差修正的基础上,首先采用加权最小二乘算法求得节点初步估计位置,将该初步估计位置作为扩展卡尔曼滤波的初始状态信息,然后采用扩展卡尔曼滤波对节点位置进行迭代求精。仿真表明,该算法具有较好的定位性能,在此基础上研究了优选信标节点参与扩展卡尔曼滤波迭代计算的最优排序方案,进一步提高了节点定位精度。
In recent years, the indoor positioning technology plays an important role in the person localization, warehousing logistics, location for coal mine, emergency rescue work in complex personnel and items positioning circumstances. The indoor positioning technology based on wireless sensor networks has particular advantages by virtue of sensor node with low cost, low power consumption and convenience to data transmission. The paper focuses on the research as follows, which how to improve the indoor positioning accuracy of nodes. Firstly, the paper analyzed the composition of wireless sensor networks indoor positioning system and node networking protocol combined in detail. Then discussed wireless sensor network based on location principle of ranging and range-free, and analyzed positioning performance of centroid and DV-Hop algorithm.
Secondly, the characteristic of RSSI-d attenuation was researched, and the indoor signal path loss model was determined by analyzing the indoor signal model. The algorithm of weighted centroid localization based on RSSI distance measurement error correction was proposed to solve the problem of RSSI ranging error. The least square error of the distance measurement compensation method was adopted in the algorithm to modify the distance measurement based RSSI that can reduce the error of distance measurement. The weighted centroid localization algorithm was employed in positioning stage; moreover, weighted factor was determined by the different influence of beacon nodes to unknown nodes to improve the positioning accuracy. Simulation results show that the accuracy of this algorithm is better than the other weighted centroid localization algorithms in performance.
Finally, the combination localization algorithm based on WLS and EKF was improved according to the large estimates deviation calculated by least squares algorithm between unknown node position and actual position. First, the weighted least squares algorithm was used to calculated node preliminary estimated position based on the RSSI ranging error correction algorithm, and the node preliminary estimated position as the EKF initial status. Then, EKF was used to iteratively calculate node location estimates. The stimulation results show that the algorithm has good positioning performance. Based on the algorithm, the optimal ordering scheme of selected beacon node involved in EKF iterative calculation was researched to further improve the node location accuracy.
其次,研究了RSSI-d衰减特性,对室内信号模型进行了分析,确定了算法所用室内信号路径损耗模型;针对RSSI方法测距定位误差较大的问题,提出了基于RSSI测距误差修正的加权质心定位算法。算法测距阶段采用基于最小二乘测距误差补偿方法对RSSI测距进行修正,以减小测距误差。定位阶段采用改进Euclidean加权质心定位算法,用信标节点对未知节点的不同影响力来确定加权因子,以提高定位精度。仿真表明,该算法精度较其它加权质心定位算法有了明显提高。
最后,针对最小二乘法计算未知节点估计位置与实际位置偏差大的问题,提出了基于WLS与EKF的混合定位算法(CLA)。该算法在RSSI测距误差修正的基础上,首先采用加权最小二乘算法求得节点初步估计位置,将该初步估计位置作为扩展卡尔曼滤波的初始状态信息,然后采用扩展卡尔曼滤波对节点位置进行迭代求精。仿真表明,该算法具有较好的定位性能,在此基础上研究了优选信标节点参与扩展卡尔曼滤波迭代计算的最优排序方案,进一步提高了节点定位精度。
In recent years, the indoor positioning technology plays an important role in the person localization, warehousing logistics, location for coal mine, emergency rescue work in complex personnel and items positioning circumstances. The indoor positioning technology based on wireless sensor networks has particular advantages by virtue of sensor node with low cost, low power consumption and convenience to data transmission. The paper focuses on the research as follows, which how to improve the indoor positioning accuracy of nodes. Firstly, the paper analyzed the composition of wireless sensor networks indoor positioning system and node networking protocol combined in detail. Then discussed wireless sensor network based on location principle of ranging and range-free, and analyzed positioning performance of centroid and DV-Hop algorithm.
Secondly, the characteristic of RSSI-d attenuation was researched, and the indoor signal path loss model was determined by analyzing the indoor signal model. The algorithm of weighted centroid localization based on RSSI distance measurement error correction was proposed to solve the problem of RSSI ranging error. The least square error of the distance measurement compensation method was adopted in the algorithm to modify the distance measurement based RSSI that can reduce the error of distance measurement. The weighted centroid localization algorithm was employed in positioning stage; moreover, weighted factor was determined by the different influence of beacon nodes to unknown nodes to improve the positioning accuracy. Simulation results show that the accuracy of this algorithm is better than the other weighted centroid localization algorithms in performance.
Finally, the combination localization algorithm based on WLS and EKF was improved according to the large estimates deviation calculated by least squares algorithm between unknown node position and actual position. First, the weighted least squares algorithm was used to calculated node preliminary estimated position based on the RSSI ranging error correction algorithm, and the node preliminary estimated position as the EKF initial status. Then, EKF was used to iteratively calculate node location estimates. The stimulation results show that the algorithm has good positioning performance. Based on the algorithm, the optimal ordering scheme of selected beacon node involved in EKF iterative calculation was researched to further improve the node location accuracy.
引文
1 Akyildiz I F, Su W, Sankarasubramaniam Y. A Survey on Sensor Networks. IEEE Communications Magazine, 2002, 40(8): 102-114
2 Wade R, Mitehel W M, Petter F. Ten Emerging Technologies that will Change the World. Technology Review, 2003, 106(1): 33-49
3 Elson J, Estrin D. Sensor Networks: A Bridge to the Physical World. Norwell: kluwer Academic Publishers, 2004: 3-20
4 Coleri S, Cheung S Y, Varaiya P. Sensor Networks for Monitoring Traffic. In Proceeding of Forty-Second Annual Allerton Conference on Communication, Control, and Computing, U. of Illinois, September 2004: 1-10
5 Pottie G, Kaiser W. Wireless Integrated Network Sensors. Communication in a Cubic Milimeter. http://robotics.eecs.berkeley.edu /~pister/SmartDust
6 Gaynor M, Moulton S, Welsh M. Integrating Wireless Sensor Networks With the Grid. IEEE Internet Computing, July & August 2004: 32-39
7宋文,王兵,周应宾.无线传感器网络技术与应用.北京:电子工业出版社, 2008
8徐勇军,安竹林,蒋文丰,姜鹏.无线传感器网络实验教程.北京:国防工业出版社, 2008
9 Cullar D, Estrin D, Strvastava M. Overview of Sensor Network. Computer, 2004, 37(8): 41-49
10尹晓丹.无线传感器网络定位算法研究.哈尔滨工业大学, 2006
11 Bahl P, Padmanabhan V N. RADAR: An in-building RF-based User Location and Tracking System. In Proceeding of the 19 Annual Joint Conference of the IEEE Computer and Communications Societies(InfoCom 2000), Tel Aviv, Israel, March 2000: 775-784
12 Harter A, Hopper A, Steggles P, Ward A, Webster P. The anatomy of a context-aware application. Proc of Mobile Computing and Networking(MOBICOM’99). Seattle, Washington, USA: ACM Press, 2000. 59-68
13 Hightower J, Boriello G, Want R. SporON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength. Technical Report UW CSE 2000-02-02, Seattle: Departm ent of Computer Science and Engineer, University of Washington, 2000
14 Priyantha N, Chakraborthy A, Balakrishnan H. The Cricket Location-support System. In Proc Int’1 Conf on Mobile Computing and Networking, Boston, MA, August 6-11, 2000: 32-43
15 Rudafshani M, Datta S. Localization in wireless sensor networks. Proceedings of IPSN. Cambridge, USA, 2007: 51-60
16 Klingbeil L, Wark T. A wireless sensor network for real-time indoor localization and motion monitoring, 2008
17 Edgar H, Callaway J. Wireless Sensor Network Architecture and Protocols. New York: CRC Press, 2004
18 Akyildiz I F, Su W, Sanakarasubramaniam Y. Wireless sensor networks: a survey. Computer Networks, 2002, 38(4): 393-422
19 Langendoen K, Reijers N. Distributed localization in wireless sensor networks: a quantitative comparison. Computer Networks, 2003, 43(4): 499-518
20 Niculescu D, Nath B. DV based positioning in ad hoc networks. Journal of Telecommunication Systems, 2003, 22(1/4): 267-280
21 Savories C, Rabay J, Langendoen K. Robust positioning algorithms for distributed ad-hoc wireless sensor networks. In: Ellis CS, eds. Proc. of the USENIX Technical Annual Conf. Monterey: USENIXPress, 2002, 317-328
22 Yu Yang, Prasanna V K, Krishnamachari B. Information Processing and Routing in Wireless Sensor Networks. World Scientific Publishing Company, December, 2006
23马玉秋,沈树群,张平,龙承志.基于无线传感器网络的定位系统研究及设计.重庆邮电学院学报(自然科学版), 2006, 18(1): 43-46
24史龙,王福豹,段渭军等.无线传感器网络Range-Free自身定位机制与算法.计算机工程与应用, 2004, 40(23): 127-130, 151
25张俊霞,汪炀,李善亮.基于无线传感器网络的定位系统设计.计算机工程与应用, 2008, 44(17): 67-70
26张婵爱等.基于RSSI的加权质心定位算法的实现.太原理工大学学报, 2009, 40(2)
27熊志广,石为人,许磊,贾传江,王海员.一种基于加权处理的三边测量定位算法.计算机工程与应用, 2010
28崔莉,鞠海玲,苗勇等.无线传感器网络研究进展,计算机研究与发展, 2005, 42(1): 163-174
29曾鹏,于海斌,梁英等.信信式无自传感器网络体系结构及应用支撑技术研究.信息与控制, 2004, 33(3): 307-313
30刘明,王婷婷,黄小燕等.基于SVM信类区域的传感器网络信信自定定质质.计质信应用, 2009, 29(4): 1064-1067
31崔逊学,赵湛,方震等.智能交通传感网技术的研究进展.第二届中国传感器网络学术会议论文集,重庆, 2008
32 Polastre J, Szewczyk R, Sharp C. The mote revolation: low power wireless sensor networks. Proceedings of the 16th Symposium on High Performance Chips(HotChips), August, 2004
33杨佑禄,童玲,田雨.基于CC2430的无自传感器网络信信的硬件电路设计.自动化信息, 2009, 93(1): 29-30
34李刚,张志宏.蜜蜂的舞蹈-ZigBee的无线网络技术和应用.电子产品世界, 2006, (3): 84-87
35崔逊学,左从菊.无线传感器网络简明教程.北京:清华大学出版社, 2009-07
36王权平,王莉. ZigBee技术简析.通讯世界, 2003, (4): 41-43
37陈寒,李仁发,乐光学.无线传感器网络中的一种分布式相对定位算法的分析与改进.计算机应用, 2006, 26(2): 310-313
38王权平,王莉. ZigBee技术及其应用.现代电信科技, 2004, (1): 33-37
39 He T, Huang C D, Blum B M. Range-Free localization schemes in large scale sensor networks. In: Proc. of the 9th Annual Int’l Conf. on Mobile Computing and Networking. San Diego: ACM Press, 2003: 81-95
40 Parket A D. IEEE 802.15.4 Standard for Low-Rate Wireless Personal Area Network(LR-WPANs). USA: The Institute of Electrical and Electronics Engineers, Inc, 2003
41 Yan-fei Liu, Cheng Wang, Xiao-jun Qiao. An Improved Design of ZigBee Wireless Sensor Network. ICCSIT 2009, 2009: 515-518
42 Harter A, Hopper A, Steggles P. The anatomy of a context-aware application. In: Proc. of the 5th Annual ACM/IEEE Int’l Conf. on Mobile Computing and Networking. Seattle: ACM Press, 2007: 59-68
43彭天笑,缭小红.基于ZigBee的WPAN构建方案.热点技术, 2006: 40-44
44 Fukui K, Fukunaga S, Tanimoto K. ZigBee technology for low-cost and low-power radio communication systems. Journal Institute of Electronics Information and Communication Engineers, 2005, 88(1): 40-45
45 Priyantha N, Balakrishnan H, Demaine E. Anchor-Free Distributed Localization in Sensor Nerworks. Technical Report TR-892, MIT Laboratory for Computer Science, April, 2003
46 Neal P, Joshua A N. Locating the nodes: Cooperative localization in wireless sensor netwoks. IEEE Signal Processing Magazine, 2005, 22(4): 54-69
47 Girod L, Estrin D. Robust range estimation using acoustic and space: A case study. In: IEEE International Conference on Intelligent Robots and Systems, 2001, Vol 3: 1112-1120
48 Girod L, Bychvskiy V, Elson J. Locating tiny sensors in time and space: A case study. In: Proceedings of the 2002 IEEE International Conference on Computer Design, Freiburg, 2002: 214-219
49 Niculescu D, Nath B. Ad Hoc Positioning System(APS) Using AOA. In: Proceeding of IEEE Conference on Computer and Communications Societies, 2003, vol.3: 1734-1743
50 Gustafsson F, Gunnarsson F. Positioning using time-difference of arrival measurements. IEEE ICASSP 2003: 553-556
51 Lewis G, Deborah E. Robust Range Estimation using Acoustic and Multimodal Sensing. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems, 2001: 1112-1120
52 Savvides A, Han C C, Srivastava M B. Dynamic fine-grained localization in ad-hoc networks of sensors. In: Proceeding of the 7th Annual International Conference on Mobile Computing and Networking, 2001: 166-179
53林玮,陈传峰.基于RSSI的无线传感器网络三角形质心定位算法.现代电子技术, 2009(02): 180-182
54 Rentala P, Musunuri R, Gandham S, Saxena U. Survey on sensor networks. Technical Report, UTDCS-33-02, University of Texas at Dallas, 2002
55陈维克,李文锋,首晰,袁兵.基于RSSI的无线传感器网络加权质心定位算法.武汉理工大学学报, 2006, 30(2): 265-268
56 Doherty L, Pister K S J, Ghaoui L E. Convex position estimation in wireless sensor networks. Proc. of the IEEE INFOCOM 2001, Anchorage: IEEE Computer and Communications Societies, 2001, 3: 1655-1663
57李佳宁.组合导航系统中的数据融合技术.哈尔滨工程大学, 2002
58丰丽军.公路汽车视觉导航中的运动目标检测与跟踪.北京:北京工业大学, 2000
59姚坤.单站无源定位跟踪方法研究.成都:电子科技大学, 2003
60于秀芬.轮式移动机器人视觉定位与目标跟踪研究.南京:南京航空航天大学, 2004
61周熙炜.基于扩展卡尔曼滤波算法的异步电机参数辨识.西安:西安理工大学, 2003
62 Estrin D, Culler D, Pister K. Connecting the Physical World with Pervasive Networks. IEEE Pervasive Computing, 2002, 1(1): 59-69
2 Wade R, Mitehel W M, Petter F. Ten Emerging Technologies that will Change the World. Technology Review, 2003, 106(1): 33-49
3 Elson J, Estrin D. Sensor Networks: A Bridge to the Physical World. Norwell: kluwer Academic Publishers, 2004: 3-20
4 Coleri S, Cheung S Y, Varaiya P. Sensor Networks for Monitoring Traffic. In Proceeding of Forty-Second Annual Allerton Conference on Communication, Control, and Computing, U. of Illinois, September 2004: 1-10
5 Pottie G, Kaiser W. Wireless Integrated Network Sensors. Communication in a Cubic Milimeter. http://robotics.eecs.berkeley.edu /~pister/SmartDust
6 Gaynor M, Moulton S, Welsh M. Integrating Wireless Sensor Networks With the Grid. IEEE Internet Computing, July & August 2004: 32-39
7宋文,王兵,周应宾.无线传感器网络技术与应用.北京:电子工业出版社, 2008
8徐勇军,安竹林,蒋文丰,姜鹏.无线传感器网络实验教程.北京:国防工业出版社, 2008
9 Cullar D, Estrin D, Strvastava M. Overview of Sensor Network. Computer, 2004, 37(8): 41-49
10尹晓丹.无线传感器网络定位算法研究.哈尔滨工业大学, 2006
11 Bahl P, Padmanabhan V N. RADAR: An in-building RF-based User Location and Tracking System. In Proceeding of the 19 Annual Joint Conference of the IEEE Computer and Communications Societies(InfoCom 2000), Tel Aviv, Israel, March 2000: 775-784
12 Harter A, Hopper A, Steggles P, Ward A, Webster P. The anatomy of a context-aware application. Proc of Mobile Computing and Networking(MOBICOM’99). Seattle, Washington, USA: ACM Press, 2000. 59-68
13 Hightower J, Boriello G, Want R. SporON: An Indoor 3D Location Sensing Technology Based on RF Signal Strength. Technical Report UW CSE 2000-02-02, Seattle: Departm ent of Computer Science and Engineer, University of Washington, 2000
14 Priyantha N, Chakraborthy A, Balakrishnan H. The Cricket Location-support System. In Proc Int’1 Conf on Mobile Computing and Networking, Boston, MA, August 6-11, 2000: 32-43
15 Rudafshani M, Datta S. Localization in wireless sensor networks. Proceedings of IPSN. Cambridge, USA, 2007: 51-60
16 Klingbeil L, Wark T. A wireless sensor network for real-time indoor localization and motion monitoring, 2008
17 Edgar H, Callaway J. Wireless Sensor Network Architecture and Protocols. New York: CRC Press, 2004
18 Akyildiz I F, Su W, Sanakarasubramaniam Y. Wireless sensor networks: a survey. Computer Networks, 2002, 38(4): 393-422
19 Langendoen K, Reijers N. Distributed localization in wireless sensor networks: a quantitative comparison. Computer Networks, 2003, 43(4): 499-518
20 Niculescu D, Nath B. DV based positioning in ad hoc networks. Journal of Telecommunication Systems, 2003, 22(1/4): 267-280
21 Savories C, Rabay J, Langendoen K. Robust positioning algorithms for distributed ad-hoc wireless sensor networks. In: Ellis CS, eds. Proc. of the USENIX Technical Annual Conf. Monterey: USENIXPress, 2002, 317-328
22 Yu Yang, Prasanna V K, Krishnamachari B. Information Processing and Routing in Wireless Sensor Networks. World Scientific Publishing Company, December, 2006
23马玉秋,沈树群,张平,龙承志.基于无线传感器网络的定位系统研究及设计.重庆邮电学院学报(自然科学版), 2006, 18(1): 43-46
24史龙,王福豹,段渭军等.无线传感器网络Range-Free自身定位机制与算法.计算机工程与应用, 2004, 40(23): 127-130, 151
25张俊霞,汪炀,李善亮.基于无线传感器网络的定位系统设计.计算机工程与应用, 2008, 44(17): 67-70
26张婵爱等.基于RSSI的加权质心定位算法的实现.太原理工大学学报, 2009, 40(2)
27熊志广,石为人,许磊,贾传江,王海员.一种基于加权处理的三边测量定位算法.计算机工程与应用, 2010
28崔莉,鞠海玲,苗勇等.无线传感器网络研究进展,计算机研究与发展, 2005, 42(1): 163-174
29曾鹏,于海斌,梁英等.信信式无自传感器网络体系结构及应用支撑技术研究.信息与控制, 2004, 33(3): 307-313
30刘明,王婷婷,黄小燕等.基于SVM信类区域的传感器网络信信自定定质质.计质信应用, 2009, 29(4): 1064-1067
31崔逊学,赵湛,方震等.智能交通传感网技术的研究进展.第二届中国传感器网络学术会议论文集,重庆, 2008
32 Polastre J, Szewczyk R, Sharp C. The mote revolation: low power wireless sensor networks. Proceedings of the 16th Symposium on High Performance Chips(HotChips), August, 2004
33杨佑禄,童玲,田雨.基于CC2430的无自传感器网络信信的硬件电路设计.自动化信息, 2009, 93(1): 29-30
34李刚,张志宏.蜜蜂的舞蹈-ZigBee的无线网络技术和应用.电子产品世界, 2006, (3): 84-87
35崔逊学,左从菊.无线传感器网络简明教程.北京:清华大学出版社, 2009-07
36王权平,王莉. ZigBee技术简析.通讯世界, 2003, (4): 41-43
37陈寒,李仁发,乐光学.无线传感器网络中的一种分布式相对定位算法的分析与改进.计算机应用, 2006, 26(2): 310-313
38王权平,王莉. ZigBee技术及其应用.现代电信科技, 2004, (1): 33-37
39 He T, Huang C D, Blum B M. Range-Free localization schemes in large scale sensor networks. In: Proc. of the 9th Annual Int’l Conf. on Mobile Computing and Networking. San Diego: ACM Press, 2003: 81-95
40 Parket A D. IEEE 802.15.4 Standard for Low-Rate Wireless Personal Area Network(LR-WPANs). USA: The Institute of Electrical and Electronics Engineers, Inc, 2003
41 Yan-fei Liu, Cheng Wang, Xiao-jun Qiao. An Improved Design of ZigBee Wireless Sensor Network. ICCSIT 2009, 2009: 515-518
42 Harter A, Hopper A, Steggles P. The anatomy of a context-aware application. In: Proc. of the 5th Annual ACM/IEEE Int’l Conf. on Mobile Computing and Networking. Seattle: ACM Press, 2007: 59-68
43彭天笑,缭小红.基于ZigBee的WPAN构建方案.热点技术, 2006: 40-44
44 Fukui K, Fukunaga S, Tanimoto K. ZigBee technology for low-cost and low-power radio communication systems. Journal Institute of Electronics Information and Communication Engineers, 2005, 88(1): 40-45
45 Priyantha N, Balakrishnan H, Demaine E. Anchor-Free Distributed Localization in Sensor Nerworks. Technical Report TR-892, MIT Laboratory for Computer Science, April, 2003
46 Neal P, Joshua A N. Locating the nodes: Cooperative localization in wireless sensor netwoks. IEEE Signal Processing Magazine, 2005, 22(4): 54-69
47 Girod L, Estrin D. Robust range estimation using acoustic and space: A case study. In: IEEE International Conference on Intelligent Robots and Systems, 2001, Vol 3: 1112-1120
48 Girod L, Bychvskiy V, Elson J. Locating tiny sensors in time and space: A case study. In: Proceedings of the 2002 IEEE International Conference on Computer Design, Freiburg, 2002: 214-219
49 Niculescu D, Nath B. Ad Hoc Positioning System(APS) Using AOA. In: Proceeding of IEEE Conference on Computer and Communications Societies, 2003, vol.3: 1734-1743
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