基于RSSI的传感器网络定位技术研究与实现
摘要
无线传感器网络被认为是21世纪最重要的技术之一,在军事、环境、工业等领域具有相当广阔的应用。其中定位技术因为与很多实际应用直接相关而尤为受到关注。本文针对WSN低成本、高灵活性的特点,对使用接收信号强度指示(RSSI,Received Signal Strength Indicator)进行定位的技术进行了研究,并在已有的软、硬件平台上完成了用于煤矿巷道中的定位系统。
本文首先研究了基于信号衰减模型的定位法。对基于RSSI的定位技术研究主要分为两个方面,基于信号衰减的定位法是其中之一。在充分研究国内外研究现状的基础上,本文通过大量的实地测量实验,分析了RSSI的概率特征,进而给出了一种加权最小二乘确定权值的方法。针对信号衰减模型参数未知或估计不准确的情况,提出了两种利用RSSI差值计算位置的方法。遮挡问题是影响RSSI定位精度的一个主要问题,本文对信号传播路径上存在人体随机遮挡的情况进行了一系列实验,通过数据统计分析,最终给出了一种加权概率模型的最大似然估计法,实验证实该方法在遮挡情况下,较ML估计能明显提高定位精度。
其次,本文对基于RSSI的定位技术的研究另外一个主要方面——经验模型定位法展开了研究。现有的经验模型定位方法(包括神经网络在内),都存在无法很好适应应用环境变化的问题。针对这一不足,本文提出一种基于动态数据库和位置信标的定位方法,并提出了保守性准则和开放性准则以控制数据库的动态调整过程,通过选取不同的准则可以达到更好的定位效果。利用多次定位结果之间的空间相关性特征,引入贝叶斯滤波器,使得定位精度进一步增加。
作为算法研究一个分支,本文利用一个独立的章节对位置估计的克拉美-罗下界(CRLB)进行了推导,并根据CRLB的性质给出了一种衡量参考节点布放对定位误差影响的因子——区域平均定位方差下界。通过这一衡量因子建立了求解参考节点最佳拓扑形式的数学模型,通过求解得到了在区域平均定位方差下界最小意义下的参考节点布放方式。其结论对于节点布设等问题具有理论上的指导意义。
在实现部分,结合对于RSSI定位技术的研究,完成了煤矿井下位置监测与管理系统中定位子系统的设计与实现工作。在嵌入式操作系统TinyOS和windows操作系统中分别利用NesC语言和Microsoft Visual C++编译环境完成了定位子系统中信息获取、有线传输和定位计算各个模块。长期测试结果表明,该系统运行稳定,能够为用户提供较高定位精度的服务。
Wireless sensor network is regarded as one of the most important technologies in 21st century. It is broadly applied in the fields of military, circumstance and industry. Positioning technique is of great importance because of its direct relative to practical application. Aimed at the low-cost and high-flexibility characters of WSN nodes, this thesis studies the positioning technology with RSSI (Received Signal Strength Indicator); it also develops a mine-laneway positioning system grounded on the existing software and hardware platform.
Firstly, positioning technology based on signal attenuation model is investigated, which is one part of the technology with RSSI. After deep research on the status quo at home and abroad, this thesis gives out the probability characters of RSSI by mounts of field measure test, and proposes a method based on weighted least-squares approach. Two positioning methods are brought forward aimed at the unknown or incorrect of signal attenuation parameters. For obstruct is one of key problems affecting RSSI based position accuracy, the situations that there are barriers on the transmitting path are tested repetitiously. From the statistical analysis, ML estimation algorithm based on a model of weighted probability is achieved, and experiments testifies this algorithm can improve position accuracy in case of obstruct environment.
Secondly, the other part of RSSI based position, namely empirical model positioning method, is discussed. The problem that algorithms cannot adapt to circumstance variety properly is exiting in all empirical models positioning method including neural network. So, this article proposes positioning algorithm on the basis of dynamic database and location beaconing as well as using two rules to control the dynamic adjustment of database. The introduction of different rules can help to gain different position effect. To get further improvement upon position accuracy, the spatial correlation of multi-position results and bayes filter are introduced.
Thirdly, CRLB of position estimation is deduced, and a factor that measure the effect of reference nodes’location on position errors called the lower bound of minimum area average position variance is also introduced. By the measuring factor, mathematical model of calculating optimum reference nodes’topology is built, and the result is gained at the lower bound of minimum area average position variance. The conclusion it is of directive significance for nodes’placement.
Finally, associated with RSSI-based positioning technology, position subsystem in miner monitoring and management system is designed and implemented. The modules such as information extraction, wire transportation and position calculation are finished using NesC and Microsoft Visual C++ based on embedded operation system TinyOS and windows system. Long-term tests show that the system runs stable and can provide high position accuracy.
本文首先研究了基于信号衰减模型的定位法。对基于RSSI的定位技术研究主要分为两个方面,基于信号衰减的定位法是其中之一。在充分研究国内外研究现状的基础上,本文通过大量的实地测量实验,分析了RSSI的概率特征,进而给出了一种加权最小二乘确定权值的方法。针对信号衰减模型参数未知或估计不准确的情况,提出了两种利用RSSI差值计算位置的方法。遮挡问题是影响RSSI定位精度的一个主要问题,本文对信号传播路径上存在人体随机遮挡的情况进行了一系列实验,通过数据统计分析,最终给出了一种加权概率模型的最大似然估计法,实验证实该方法在遮挡情况下,较ML估计能明显提高定位精度。
其次,本文对基于RSSI的定位技术的研究另外一个主要方面——经验模型定位法展开了研究。现有的经验模型定位方法(包括神经网络在内),都存在无法很好适应应用环境变化的问题。针对这一不足,本文提出一种基于动态数据库和位置信标的定位方法,并提出了保守性准则和开放性准则以控制数据库的动态调整过程,通过选取不同的准则可以达到更好的定位效果。利用多次定位结果之间的空间相关性特征,引入贝叶斯滤波器,使得定位精度进一步增加。
作为算法研究一个分支,本文利用一个独立的章节对位置估计的克拉美-罗下界(CRLB)进行了推导,并根据CRLB的性质给出了一种衡量参考节点布放对定位误差影响的因子——区域平均定位方差下界。通过这一衡量因子建立了求解参考节点最佳拓扑形式的数学模型,通过求解得到了在区域平均定位方差下界最小意义下的参考节点布放方式。其结论对于节点布设等问题具有理论上的指导意义。
在实现部分,结合对于RSSI定位技术的研究,完成了煤矿井下位置监测与管理系统中定位子系统的设计与实现工作。在嵌入式操作系统TinyOS和windows操作系统中分别利用NesC语言和Microsoft Visual C++编译环境完成了定位子系统中信息获取、有线传输和定位计算各个模块。长期测试结果表明,该系统运行稳定,能够为用户提供较高定位精度的服务。
Wireless sensor network is regarded as one of the most important technologies in 21st century. It is broadly applied in the fields of military, circumstance and industry. Positioning technique is of great importance because of its direct relative to practical application. Aimed at the low-cost and high-flexibility characters of WSN nodes, this thesis studies the positioning technology with RSSI (Received Signal Strength Indicator); it also develops a mine-laneway positioning system grounded on the existing software and hardware platform.
Firstly, positioning technology based on signal attenuation model is investigated, which is one part of the technology with RSSI. After deep research on the status quo at home and abroad, this thesis gives out the probability characters of RSSI by mounts of field measure test, and proposes a method based on weighted least-squares approach. Two positioning methods are brought forward aimed at the unknown or incorrect of signal attenuation parameters. For obstruct is one of key problems affecting RSSI based position accuracy, the situations that there are barriers on the transmitting path are tested repetitiously. From the statistical analysis, ML estimation algorithm based on a model of weighted probability is achieved, and experiments testifies this algorithm can improve position accuracy in case of obstruct environment.
Secondly, the other part of RSSI based position, namely empirical model positioning method, is discussed. The problem that algorithms cannot adapt to circumstance variety properly is exiting in all empirical models positioning method including neural network. So, this article proposes positioning algorithm on the basis of dynamic database and location beaconing as well as using two rules to control the dynamic adjustment of database. The introduction of different rules can help to gain different position effect. To get further improvement upon position accuracy, the spatial correlation of multi-position results and bayes filter are introduced.
Thirdly, CRLB of position estimation is deduced, and a factor that measure the effect of reference nodes’location on position errors called the lower bound of minimum area average position variance is also introduced. By the measuring factor, mathematical model of calculating optimum reference nodes’topology is built, and the result is gained at the lower bound of minimum area average position variance. The conclusion it is of directive significance for nodes’placement.
Finally, associated with RSSI-based positioning technology, position subsystem in miner monitoring and management system is designed and implemented. The modules such as information extraction, wire transportation and position calculation are finished using NesC and Microsoft Visual C++ based on embedded operation system TinyOS and windows system. Long-term tests show that the system runs stable and can provide high position accuracy.
引文
[1]于宏毅,李鸥,张效义.无线传感器网络理论技术与实现[M].北京:国防工业出版社,2008:3-38.
[2] Asis Nasipuri, Kai Li, A Directionality based Location Discovery Scheme for Wireless Sensor Networks. WSNA’02, Atlanta, Georgia, USA. 2002:105-111.
[3] J. Li, J. Jannotti, D. S. J DeCoutuo, D. R. Karger, and R. Morris. A scalable Location service for geographic ad hoc routing [C]. Proceedings of ACM MOBICOM, 2000, 8: 120-130.
[4]陈红阳.基于测距技术的无线传感器网络定位技术研究[D].2006,3.
[5] B. Karp and H. T. kung. GPSR: Greedy Perimeter stateless routing for wireless networks[C]. ACM/IEEE International Conference on Mobile Computing and Networking (MOB- ICOM),2000,8: 243-254.
[6] S.Meguerdichian, F.Koushanfar, M Potkonjak, and M.B.Srivastava[C]. Coverage Problems in wireless ad hoc sensor networks. Proceedings of INFOCOM, 2001: 1380-1387.
[7] J.Heidemann. Using geospatial information in sensor networks[C]. ACM/IEEE International Conference on Mobile Computing and Networking (MOB- ICOM), 2000,8: 243-254.
[8] H. Gupta, S. R. Das, and Q. Gu. Connected Sensor Cover: Self-Organization of Sensor Networks for Efficient Query Execution. Proc. of mobiHoc 2003, Annapolis, Maryland, 2003,6: 55-67.
[9] Tian He, Chengdu Huang, Brian M Blum et al. Rangr-free Localization Schemes in Large Scale Sensor Networks[C]. Proceedings of 9th annual international conference on Mobile computing and networking (MobiCom), San Diego, California, USA:ACM Press, 2003: 81-95.
[10] N.Bulusu, J. Heidemann, D. Estrin. GPS-less low-cost outdoor localization for very small devices[C],IEEE Person. Commun.7(5),2000:28-34.
[11] Capkun S, Hamdi M, Hubaux J. Gps-Free position in mobile ad hoc networks[C]. Proc of Hawaii Int’1. Conf. System Sciences. 2001.3481-3490.
[12] Cesare Apippi, Giovanni, Wireless Sensor Networks and Radio Localization: a Metrological Analysis of the MIC2 received signal strenth indicator[C],29th Annual IEEE International Conference on Local Computer Networks (LCN’04),tampa, Florida, USA, 2004:579-582.
[13] D.Niculescu and B. Nath, Ad-hoc positioning system[C]. Proceeding of IEEE Global Communications Conference (GLOBECOM). 2001:2926-1931.
[14] R.Nagpal, Organization Global Coorinate System form Local Information on an Amorphous Computer. A.I. Memo1666, MITA.I. Laboratory,1999: 1-10.
[15]史龙,王福豹,段渭军,任丰原.无线传感器网络Range-Free自身定位机制与算法[J],计算机工程与应用,2004, 2:857-866.
[16] D.Niculescu and B. Nath, Ad-hoc positioning system (APS) using AOA[C]. Proceedings of the 20st Annual Jint Conference of the IEEE Computer and Communications Societies(INFO- COM-22), 2003,4: 1734-1743.
[17] A. Savvides, C.-C. Han, and M. Srivastava, Dynamic fine-grained localization in ad-hoc networks of sensors[C]. Proceeding of 7th ACM International Conference on Mobile Computing and Networking (MOBICOM), Rome, Italy, 2001.166-179.
[18] J.chen,K.Yao,R.Hudson, Source localization and beamforming[J], IEEE Signal Process. Mag.19(2),2002:30-39.
[19] L.Doherty, K.Pister, L.E. Ghaoui, Convex Position estimation in wireless sensor networks[C]. IEEE Infocom 2001, Anchorage, AK, 2001:1655-1663.
[20] A. Savvides, H. Park, and M. B. Srivastava. The bits and flops of the n-hop multiateration primitive for node localization problems[C]. Proc. Int. Workshop Sensor Nets. Appl. 2002:112-121.
[21] P. Bahl and V. padmanabhan. RADAR: An in-building RF-based user location and tracking system[C]. Proceeding of INFOCOM.2000,3:775-784.
[22] K. Whitehouse. The design of calamari: an ad-hoc localization. System for sensor networks [D]. University of. California at Berkerley, 2002:1-25.
[23]张洁颖等.基于RSSI和LQI的动态距离估计法[J].电子测量技术.2007,(30)2:142-145.
[24]任维政等.基于RSSI的测距差分修正定位算法[J].传感技术学报.2008,7(21)7:1247- 1250.
[25]陈维克等.基于RSSI的无线传感器网络加权质心定位法[J].武汉理工大学学报(交通科学与工程版) . 2006,4(30)2: 265-268.
[26]朱剑等.基于RSSI均值的等边三角形定位算法[J].东北大学学报(自然科学版). 2007,8(28) :1094-1097.
[27] Claude Takenga, Chen Xi, Kyandoghere Kyamakya. Fusion of Neural Network positioning and Database Correlation in localizing a Mobile Terminal[C]. Proceedings of the 2006 International Conference on Wireless Networks (ICMN 06), Las Vegas, USA, 2006: 26-29.
[28] Uzair Ahmad, Andrey Gavrilov, Sungyoung Lee. In building Localization Using Neural Networks[C]. Proceedings of IEEE International Conference on Intelligent Engineering Systems.2006: 1-6.
[29]杨大成等.移动传播环境理论基础、分析方法和建模技术[M].机械工业出版社.2003:70-149.
[30]孙利民、李建中等.无线传感器网络[M].北京:清华大学出版社.2005:135-155.
[31] Neal Patwari. Relative Location Estimation in Wireless Sensor Networks[C]. IEEE Transactions on signal processing. VOL. 51. NO. 8, 2003. 8:2137-2147.
[32] Neal Patwari, Robert J. O’Dea, Yanwei Wang. Relative Location Estimation in Wireless Networks[C]. In Proc. IEEE VTC, vol.2, 2001:1149-1153.
[33] Kaemarungsi, K.; Krishnamurthy, P. Modeling of indoor positioning systems based on location fingerprinting. Proceeding of Infocom’2004. Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies[C], 2004:2: 1012-1022.
[34] Guolin Sun.Wei Guo, Robust mobile geo-location algorithm based on LS-SVM[C]. IEEE transactions on vehicular technology, New York, 2005: 1037-1041.
[35] Mauro Brunato, Roberto Battiti. Statistical learning theory for location fingerprinting in wireless LANs[R]. Italy, 2007.
[36] S R Theodore. Wireless Communications: Principles and Practice (2nd Edition) [M]. Prentice Hall Press, 2001, 69 -138.
[37] Paramvir.B, Venkata. N, Padmanabhan. RADAR: An in-building RF-based user location and tracking system. Proceeding of Infocom’2000,Tel Aviv, Israel: IEEE Computer and Communications Societies [C], 2000: 2: 775-584.
[38] C Savarese, J Rabay, K Langendoen, Robust Positioning Algorithms for Distributed Ad hoc Wireless Sensor Networks [A].Carla Schlatter Ellis. Proceedings of The USENIX Technical Annual Conference [C]. Monterey: USENIX Press, 2002:317-327.
[39]王建刚,王福豹,段渭军.加权最小二乘估计在无线传感器网络定位中的应用[J].计算机应用研究,2006,23 (9):41- 43.
[40] Ji, X; Zha, H. Robust sensor localization algorithm in wireless ad hoc sensor networks[C]. Computer Communications and Networks, 2003. ICCC 2003.Proceedings. The 12th International Conference on 20-22 Oct. 2003: 527-532.
[41]沈建华,杨艳琴等.MSP430系列16位超低功耗单片机原理与应用[M].北京:清华大学出版社.2004:132-166.
[42]唐应辉,唐小我.排队论-基础与分析技术[M].北京:科学出版社.2006:47-50.
[43] Y. T. Chan, A Simple and Efficient Estimator for Hyperbolic Location[C]. IEEE Transactions on Signal Proceeding. VOL, 42, NO. 8. 1994,8:1905-1915.
[44] Y. P. Zhang, G. X. Zheng, and J. H. Sheng. Radio Propagation at 900 MHz in Underground Coal Mines. IEEE Transactions on Antennas and Propagation, VOL. 49, NO, 5, 2001,5: 757-762.
[45]孙佩刚,赵海等.混沌三角形定位参考节点选择算法[J].计算机研究与发展.2007,44(12):1987-1995.
[46]关沫等.普适计算中定位服务的参考点选择及误差分析[J].电子学报.2005,33(8):1425-1431.
[47]李晶,王福豹,段渭军.无线传感器网络中TinyOS的研究[J].计算机测量与控制,2006, 14(6): 838-840.
[48] Gay D, Levis P, Culler D. nesC1.1 Language Reference Manual[EB/OL]. nesC网站, 2005-12-15.
[49]盛聚,谢式千等.概率论与数理统计[M].北京:高等教育出版社.2001:241-252.
[50] W. H. Foy. Position Location Solutions by Taylor Series Estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 1976, 12(2): 187-194.
[2] Asis Nasipuri, Kai Li, A Directionality based Location Discovery Scheme for Wireless Sensor Networks. WSNA’02, Atlanta, Georgia, USA. 2002:105-111.
[3] J. Li, J. Jannotti, D. S. J DeCoutuo, D. R. Karger, and R. Morris. A scalable Location service for geographic ad hoc routing [C]. Proceedings of ACM MOBICOM, 2000, 8: 120-130.
[4]陈红阳.基于测距技术的无线传感器网络定位技术研究[D].2006,3.
[5] B. Karp and H. T. kung. GPSR: Greedy Perimeter stateless routing for wireless networks[C]. ACM/IEEE International Conference on Mobile Computing and Networking (MOB- ICOM),2000,8: 243-254.
[6] S.Meguerdichian, F.Koushanfar, M Potkonjak, and M.B.Srivastava[C]. Coverage Problems in wireless ad hoc sensor networks. Proceedings of INFOCOM, 2001: 1380-1387.
[7] J.Heidemann. Using geospatial information in sensor networks[C]. ACM/IEEE International Conference on Mobile Computing and Networking (MOB- ICOM), 2000,8: 243-254.
[8] H. Gupta, S. R. Das, and Q. Gu. Connected Sensor Cover: Self-Organization of Sensor Networks for Efficient Query Execution. Proc. of mobiHoc 2003, Annapolis, Maryland, 2003,6: 55-67.
[9] Tian He, Chengdu Huang, Brian M Blum et al. Rangr-free Localization Schemes in Large Scale Sensor Networks[C]. Proceedings of 9th annual international conference on Mobile computing and networking (MobiCom), San Diego, California, USA:ACM Press, 2003: 81-95.
[10] N.Bulusu, J. Heidemann, D. Estrin. GPS-less low-cost outdoor localization for very small devices[C],IEEE Person. Commun.7(5),2000:28-34.
[11] Capkun S, Hamdi M, Hubaux J. Gps-Free position in mobile ad hoc networks[C]. Proc of Hawaii Int’1. Conf. System Sciences. 2001.3481-3490.
[12] Cesare Apippi, Giovanni, Wireless Sensor Networks and Radio Localization: a Metrological Analysis of the MIC2 received signal strenth indicator[C],29th Annual IEEE International Conference on Local Computer Networks (LCN’04),tampa, Florida, USA, 2004:579-582.
[13] D.Niculescu and B. Nath, Ad-hoc positioning system[C]. Proceeding of IEEE Global Communications Conference (GLOBECOM). 2001:2926-1931.
[14] R.Nagpal, Organization Global Coorinate System form Local Information on an Amorphous Computer. A.I. Memo1666, MITA.I. Laboratory,1999: 1-10.
[15]史龙,王福豹,段渭军,任丰原.无线传感器网络Range-Free自身定位机制与算法[J],计算机工程与应用,2004, 2:857-866.
[16] D.Niculescu and B. Nath, Ad-hoc positioning system (APS) using AOA[C]. Proceedings of the 20st Annual Jint Conference of the IEEE Computer and Communications Societies(INFO- COM-22), 2003,4: 1734-1743.
[17] A. Savvides, C.-C. Han, and M. Srivastava, Dynamic fine-grained localization in ad-hoc networks of sensors[C]. Proceeding of 7th ACM International Conference on Mobile Computing and Networking (MOBICOM), Rome, Italy, 2001.166-179.
[18] J.chen,K.Yao,R.Hudson, Source localization and beamforming[J], IEEE Signal Process. Mag.19(2),2002:30-39.
[19] L.Doherty, K.Pister, L.E. Ghaoui, Convex Position estimation in wireless sensor networks[C]. IEEE Infocom 2001, Anchorage, AK, 2001:1655-1663.
[20] A. Savvides, H. Park, and M. B. Srivastava. The bits and flops of the n-hop multiateration primitive for node localization problems[C]. Proc. Int. Workshop Sensor Nets. Appl. 2002:112-121.
[21] P. Bahl and V. padmanabhan. RADAR: An in-building RF-based user location and tracking system[C]. Proceeding of INFOCOM.2000,3:775-784.
[22] K. Whitehouse. The design of calamari: an ad-hoc localization. System for sensor networks [D]. University of. California at Berkerley, 2002:1-25.
[23]张洁颖等.基于RSSI和LQI的动态距离估计法[J].电子测量技术.2007,(30)2:142-145.
[24]任维政等.基于RSSI的测距差分修正定位算法[J].传感技术学报.2008,7(21)7:1247- 1250.
[25]陈维克等.基于RSSI的无线传感器网络加权质心定位法[J].武汉理工大学学报(交通科学与工程版) . 2006,4(30)2: 265-268.
[26]朱剑等.基于RSSI均值的等边三角形定位算法[J].东北大学学报(自然科学版). 2007,8(28) :1094-1097.
[27] Claude Takenga, Chen Xi, Kyandoghere Kyamakya. Fusion of Neural Network positioning and Database Correlation in localizing a Mobile Terminal[C]. Proceedings of the 2006 International Conference on Wireless Networks (ICMN 06), Las Vegas, USA, 2006: 26-29.
[28] Uzair Ahmad, Andrey Gavrilov, Sungyoung Lee. In building Localization Using Neural Networks[C]. Proceedings of IEEE International Conference on Intelligent Engineering Systems.2006: 1-6.
[29]杨大成等.移动传播环境理论基础、分析方法和建模技术[M].机械工业出版社.2003:70-149.
[30]孙利民、李建中等.无线传感器网络[M].北京:清华大学出版社.2005:135-155.
[31] Neal Patwari. Relative Location Estimation in Wireless Sensor Networks[C]. IEEE Transactions on signal processing. VOL. 51. NO. 8, 2003. 8:2137-2147.
[32] Neal Patwari, Robert J. O’Dea, Yanwei Wang. Relative Location Estimation in Wireless Networks[C]. In Proc. IEEE VTC, vol.2, 2001:1149-1153.
[33] Kaemarungsi, K.; Krishnamurthy, P. Modeling of indoor positioning systems based on location fingerprinting. Proceeding of Infocom’2004. Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies[C], 2004:2: 1012-1022.
[34] Guolin Sun.Wei Guo, Robust mobile geo-location algorithm based on LS-SVM[C]. IEEE transactions on vehicular technology, New York, 2005: 1037-1041.
[35] Mauro Brunato, Roberto Battiti. Statistical learning theory for location fingerprinting in wireless LANs[R]. Italy, 2007.
[36] S R Theodore. Wireless Communications: Principles and Practice (2nd Edition) [M]. Prentice Hall Press, 2001, 69 -138.
[37] Paramvir.B, Venkata. N, Padmanabhan. RADAR: An in-building RF-based user location and tracking system. Proceeding of Infocom’2000,Tel Aviv, Israel: IEEE Computer and Communications Societies [C], 2000: 2: 775-584.
[38] C Savarese, J Rabay, K Langendoen, Robust Positioning Algorithms for Distributed Ad hoc Wireless Sensor Networks [A].Carla Schlatter Ellis. Proceedings of The USENIX Technical Annual Conference [C]. Monterey: USENIX Press, 2002:317-327.
[39]王建刚,王福豹,段渭军.加权最小二乘估计在无线传感器网络定位中的应用[J].计算机应用研究,2006,23 (9):41- 43.
[40] Ji, X; Zha, H. Robust sensor localization algorithm in wireless ad hoc sensor networks[C]. Computer Communications and Networks, 2003. ICCC 2003.Proceedings. The 12th International Conference on 20-22 Oct. 2003: 527-532.
[41]沈建华,杨艳琴等.MSP430系列16位超低功耗单片机原理与应用[M].北京:清华大学出版社.2004:132-166.
[42]唐应辉,唐小我.排队论-基础与分析技术[M].北京:科学出版社.2006:47-50.
[43] Y. T. Chan, A Simple and Efficient Estimator for Hyperbolic Location[C]. IEEE Transactions on Signal Proceeding. VOL, 42, NO. 8. 1994,8:1905-1915.
[44] Y. P. Zhang, G. X. Zheng, and J. H. Sheng. Radio Propagation at 900 MHz in Underground Coal Mines. IEEE Transactions on Antennas and Propagation, VOL. 49, NO, 5, 2001,5: 757-762.
[45]孙佩刚,赵海等.混沌三角形定位参考节点选择算法[J].计算机研究与发展.2007,44(12):1987-1995.
[46]关沫等.普适计算中定位服务的参考点选择及误差分析[J].电子学报.2005,33(8):1425-1431.
[47]李晶,王福豹,段渭军.无线传感器网络中TinyOS的研究[J].计算机测量与控制,2006, 14(6): 838-840.
[48] Gay D, Levis P, Culler D. nesC1.1 Language Reference Manual[EB/OL]. nesC网站, 2005-12-15.
[49]盛聚,谢式千等.概率论与数理统计[M].北京:高等教育出版社.2001:241-252.
[50] W. H. Foy. Position Location Solutions by Taylor Series Estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 1976, 12(2): 187-194.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |